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Sengupta, Biswa. "Information encoding and energy consumption in single neuron models." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610020.
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Csereklyei, Zsuzsanna, and Stefan Humer. "Modelling Primary Energy Consumption under Model Uncertainty." WU Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3706/1/wp147.pdf.
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This paper examines the long-term relationship between primary energy consumption and other key macroeconomic variables, including real GDP, labour force, capital stock and technology, using a panel dataset for 64 countries over the period 1965-2009. Deploying panel error correction models, we find that there is a positive relationship running from physical capital, GDP, and population to primary energy consumption. We observe however a negative relationship between total factor productivity and primary energy usage. Significant differences arise in the magnitude of the cointegration coefficients, when we allow for differences in geopolitics and wealth levels. We also argue that inference on the basis of a single model without taking model uncertainty into account can lead to biased conclusions. Consequently, we address this problem by applying simple model averaging techniques to the estimated panel cointegration models. We find that tackling the uncertainty associated with selecting a single model with model averaging techniques leads to a more accurate representation of the link between energy consumption and the other macroeconomic variables, and to a significantly increased out-of-sample forecast performance. (authors' abstract)<br>Series: Department of Economics Working Paper Series
3
Zhao, Haixiang. "Artificial Intelligence Models for Large Scale Buildings Energy Consumption Analysis." Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-00658767.
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The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, occupancy and their behaviors, the operation of sub-level components like Heating, Ventilation and Air-Conditioning (HVAC) system. This complex property makes the prediction, analysis, or fault detection/diagnosis of building energy consumption very difficult to accurately and quickly perform. This thesis mainly focuses on up-to-date artificial intelligence models with the applications to solve these problems. First, we review recently developed models for solving these problems, including detailed and simplified engineering methods, statistical methods and artificial intelligence methods. Then we simulate energy consumption profiles for single and multiple buildings, and based on these datasets, support vector machine models are trained and tested to do the prediction. The results from extensive experiments demonstrate high prediction accuracy and robustness of these models. Second, Recursive Deterministic Perceptron (RDP) neural network model is used to detect and diagnose faulty building energy consumption. The abnormal consumption is simulated by manually introducing performance degradation to electric devices. In the experiment, RDP model shows very high detection ability. A new approach is proposed to diagnose faults. It is based on the evaluation of RDP models, each of which is able to detect an equipment fault.Third, we investigate how the selection of subsets of features influences the model performance. The optimal features are selected based on the feasibility of obtaining them and on the scores they provide under the evaluation of two filter methods. Experimental results confirm the validity of the selected subset and show that the proposed feature selection method can guarantee the model accuracy and reduces the computational time.One challenge of predicting building energy consumption is to accelerate model training when the dataset is very large. This thesis proposes an efficient parallel implementation of support vector machines based on decomposition method for solving such problems. The parallelization is performed on the most time-consuming work of training, i.e., to update the gradient vector f. The inner problems are dealt by sequential minimal optimization solver. The underlying parallelism is conducted by the shared memory version of Map-Reduce paradigm, making the system particularly suitable to be applied to multi-core and multiprocessor systems. Experimental results show that our implementation offers a high speed increase compared to Libsvm, and it is superior to the state-of-the-art MPI implementation Pisvm in both speed and storage requirement.
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Sharma, Roberta Ferreira Carrijo. "Projeção da demanda energética no setor industrial brasileiro." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264547.
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Orientador: Sérgio Valdir Bajay<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-16T12:23:43Z (GMT). No. of bitstreams: 1
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Previous issue date: 2009<br>Resumo: O setor industrial brasileiro é responsável por cerca de 20% do Produto Interno Bruto e 35% do consumo energético nacional. Trata-se de um setor bastante heterogêneo, com inúmeras cadeias produtivas envolvendo diversos usos finais da energia. Projeções da demanda energética deste setor devem levar em conta esta heterogeneidade, através de uma desagregação adequada. Os modelos de projeção desta demanda também devem ser capazes de simular mudanças tecnológicas, impactos de programas de eficiência energética e o efeito de variáveis macroeconômicas. Neste trabalho, o setor industrial do País foi dividido em quatorze segmentos, que se caracterizam por serem energointensivos, ou possuírem forte participação no Produto Interno Bruto. Esta dissertação apresenta, inicialmente, uma ampla análise retrospectiva do desempenho econômico e do consumo energético destes segmentos industriais, utilizando vários parâmetros capazes de capturar as principais influências tecnológicas e econômicas neste consumo. Em seguida, são apresentadas projeções, a longo prazo, da demanda energética destes segmentos,utilizando o modelo de desagregação estrutural. Este modelo utiliza parâmetros levantados
durante a análise retrospectiva e permite a simulação de rupturas dos padrões históricos das demandas energéticas a serem projetadas. Através do uso de cenários alternativos, foi possível explorar os impactos, nestas demandas, de diversas configurações de crescimento econômico e de políticas de conservação de energia.<br>Abstract: Brazilian industry is responsible for about 20% of the Gross Domestic Product and 35% of the national energy consumption. It is a very heterogeneous sector of the economy, with several productive chains involving various end-uses for energy. Energy demand forecasts for this industry should take into account this heterogeneity, through an adequate disaggregation. The forecasting models for this demand should also be able to simulate technological changes, the impacts of energy efficiency programs and the effect of macroeconomic variables. The industrial sector of the country was divided here into fourteen branches, which are either energy-intensive, or have a strong contribution to Gross Domestic Product. This thesis presents, initially, a broad retrospective analysis of the economic performance and energy consumption of these industrial branches, using several parameters capable to capture the main technological and economic influences in this consumption. Then, long-term forecasts of the energy demand of these branches are produced, using the structural disaggregation model. This model uses parameters obtained during the retrospective analysis and allows the simulation of ruptures in the historical pattern of the energy demands to be projected. Using alternative scenarios, it was possible to explore the impacts, in these demands, of several schemes of economic growth and energy conservation policies.<br>Mestrado<br>Planejamento de Sistemas Energeticos<br>Mestre em Planejamento de Sistemas Energéticos
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Al, Nur Maglub. "Automobile fuel efficiency and gasoline demand : models and inter-country comparisons." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333056.
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Hamzavi-Rad, S. "The optimal control of energy consumption in the United States Economy." Thesis, University of Essex, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381927.
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Shrestha, Rita. "Energy planning and policies in nepal." Thesis, Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19131884.
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SOARES, LACIR JORGE. "MULTILEVEL DIAGNOSTIC OF LINEAR HIERARQUICAL MODELS APPLIED TO ELECTRICAL ENERGY CONSUMPTION DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1998. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8682@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Nesta dissertação é apresentado e discutido um modelo
hierárquico para dados de medições repetidas. Trata-se de
um modelo de dois níveis em que o primeiro nível considera-
se as ocasiões e no segundo as medições realizadas da
variável de interesse.
Os modelos hierárquicos propostos foram testados
utilizando-se dados reais do sistema da educação básica do
Brasil - SAED/95, para mostrar os modelos hierárquicos
simples, e dados de consumo de energia elétrica da área de
concessão da LIGHT no Estado do Rio de Janeiro para os
modelos de medidas repetidas.<br>In this dissertation, it was developed a multilevel
approach to diagnose hierarchical linear models for
repeated measures data. Since these models include
multiple source of error, it defined several different
types of residuals. The models are a 2-level hierarchy,
i.e. that of measurements nested within subject where in
the first level it was considered the number of occasions
and in the second level the measures of the interested
variable.
Among other things, the hierarchical linear models were
analysed using real data of the Brazilian Basic Education
System (SAEB/95) in order to illustrate simple
hierarchical models.
In the multilevel structures it was used repeated measures
of energy consumption data from LIGHT, one of the
Electrical Energy Utilities of Rio de Janeiro state.
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Alhaj, Hasan Ola. "Optimization of building energy consumption using simplified models and new control methods." Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10162/document.
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L'inquiétude croissante concernant le futur des ressources énergétique a fait de l'optimisation énergétique une priorité dans tous les secteurs. De nombreux sujets de recherche se sont focalisés sur celui du bâtiment étant le principal consommateur d'énergie, en particulier à cause de ses besoins en chauffage. Beaucoup de propositions pour réduire la consommations ont été faites. Ceux-ci vont de l'amélioration de l'isolation au changement du système de gestion du thermostat en passant par la formation des occupants à une meilleure gestion de leur bâtiment. Cette thèse propose une nouvelle méthode de contrôle qui permet de minimiser la consommation énergétique et dépenses budgétaires. La méthode génère un planning énergétique sur une période de temps pré-définie, ceci en prenant compte du confort thermique des occupants. Elle est basée sur l'application de la méthode de Monte Carlo, un générateur aléatoire appliqué au système de chauffage. L'objectif est de déterminer le planning de chauffage optimal, qui respecte les trois contraintes suivantes: - Le confort thermique des résidents; - La minimisation de l'énergie consommée / du budget; - Le déplacement de la charge. De plus, pour tester cette méthode, l'identification du comportement thermique du bâtiment a été requise. De ce fait, un modèle thermique du bâtiment a été développé. Ce modèle a été volontairement simplifié afin de l'intégrer plus simplement dans le processus de contrôle. De plus, une nouvelle approche d'identification thermique du bâtiment aussi bien qu'une nouvelle méthode de contrôle en temps réel ont été présentées<br>With the highly developing concerns about the future of energy resources, the optimization of energy consumption becomes a must in all sectors. A lot of research was dedicated to buildings regarding that they constitute the highest energy consuming sector mainly because of their heating needs. Many proposals of new strategies to minimize building consumption were done. These proposals vary between recommending better insulation, advising change in occupants' behavior and changing the heating control management. This thesis proposes a new control method that helps minimizing the heating consumption and expenses. This method generates an energy plan over a defined prediction horizon respecting the occupants’ thermal comfort. It is based on the application of Monte Carlo method, i.e., a random generator for the heating system scenarios. The aim is to determine the optimal heating plan for the prediction horizon that fulfills the constraints regarding the following three factors: • The thermal comfort of occupants; • The minimization of the energy consumption/expenses; • Load shifting. However, to test this method, an identification of the building thermal behavior was needed. Thus, a building thermal model to simulate the building behavior was developed. This model was meant to be simplified in order to better integrate it in the control process. Furthermore, a new parameter estimation approach as well as a real time temperature control method are presented to ensure the implementation of the optimal predicted plan
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Mouzon, Gilles C. "Operational methods and models for minimization of energy consumption in a manufacturing environment." Diss., Wichita State University, 2008. http://hdl.handle.net/10057/1954.
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This dissertation develops operational methods for minimization of energy consumption of manufacturing equipments. Using the frameworks developed, a significant amount of energy can be saved when non-bottleneck and/or underutilized machines/equipment are turned on during a long idle time. In this dissertation, mathematical models are developed for multi-objective problems (on single or parallel machines) with minimization of a scheduling criteria and minimization) of total energy consumption. Metaheuristics approaches have been developed to solve the resulting mathematical models. Genetic algorithm, greedy randomized adaptive search procedure, and hybrid metaheuristic based approaches are utilized to obtain good approximate sets of non-dominated solutions in reasonable amount of time for different optimization problems. The decision-maker can use the results from these frameworks to plan for energy efficient production. The methods to obtain an approximate pareto front is combined with a selection method such as the Analytic Hierarchy Process to obtain a full schedule that minimizes other secondary objectives. Further research includes studying the reliability of the machine under repeated turned-off and turned-on. Also a maintenance model could be developed to include the energy minimization framework. This additional model would be useful in scheduling maintenance team with the objective of decreasing the costs associated with breakdowns including the energy-related cost. Another area of interest might be on developing a model to minimize energy consumption and scheduling objectives for machines with multi-state sleep modes.<br>Thesis (Ph.D.) - Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering
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Mouzon, Gilles C. Yildirim Mehmet Bayram. "Operational methods and models for minimization of energy consumption in a manufacturing environment /." A link to full text of this dissertation in SOAR, 2008. http://hdl.handle.net/10057/1954.
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SILVA, Verônica Conceição Oliveira da. "Modelos de avaliação de desempenho e consumo de energia em dispositivos móveis." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/16371.
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Previous issue date: 2015-08-07<br>A popularização dos dispositivos móveis e o intenso crescimento das aplicações destinadas
a estes dispositivos favorece tais elementos como ferramentas de trabalho para inúmeras
áreas, incluindo atenção saúde, uma área em constante evolução. A tecnologia móvel auxilia
a área médica oferecendo facilidades e novos recursos às equipes médicas e pacientes, devido
a isto, os dispositivos móveis e suas aplicações dedicadas contribuem como uma ferramenta
de apoio no contexto de atenção à saúde. As aplicações de mensagens instantâneas facilitam
a comunicação, de forma que as equipes médicas possam ter acesso constante aos dados de
monitoramento de seus pacientes através do uso de seus dispositivos móveis. O dispositivo
móvel é um elemento integrante do ambiente de computação móvel em nuvem e conhecer o
tempo médio em que este dispositivo irá funcionar sem que apresente falhas é o ponto de partida
para avaliar o desempenho desta ferramenta utilizada pelas equipes de atenção à saúde. O
dispositivo móvel é composto por diversos elementos incluindo bateria e as interfaces de rede
que são identificadas como componentes relevantes na disponibilidade para utilização constante
das aplicações nestes dispositivos. A utilização de modelos de avaliação de desempenho auxilia
na previsibilidade do comportamento do dispositivo móvel no ambiente, desta forma é adotado
modelos de diagramas de blocos para avaliar a confiabilidade e disponibilidade do dispositivo,
juntamente com outros modelos a fim de avaliar o desempenho de elementos específicos do
dispositivo móvel, tais como, a conectividade das interfaces de rede através do uso de redes de
Petri e o consumo de energia através de cadeias de Markov; com base nos modelos elaborados
distintos cenários são avaliados para comparar o desempenho do consumo de energia das quatro
estratégias de sincronismo mais usuais de aplicações de comunicação instantânea e desta forma
compreender o impacto de adoção destas no desenvolvimento das aplicações voltada à atenção à
saúde. Os modelos elaborados permitem avaliar o desempenho dos protocolos de aplicação e o
impacto das interfaces de rede do dispositivo para preservar o recurso de energia do dispositivo.<br>More and more mobile devices and the sharp increase in applications for these devices
favors such elements as work tools for many areas, including health care, an area in constant
evolution. Mobile technology helps medical facilities and providing new resources to medical
staff and patients, due to this, mobile devices and their dedicated applications contribute as a
support tool in the context health care. The instant messaging applications facilitate communication
so that medical staff can have constant access to their patient’s monitoring data through
their mobile devices use. The mobile device is a Mobile Cloud Computing component element.
To know the average time this device will function without presenting faults is the starting
point for assessing this tool’s performance used by health care teams. The mobile consists
of various elements including battery and network interfaces, and these identified as relevant
components in readiness for constant use of applications on these devices. The evaluation models
help predictability mobile device behavior in the environment. The block diagrams model was
adopted to assess the device’s reliability and availability. Like other models to evaluate the
specific elements performance of the mobile device. The network interfaces connectivity through
the Petri networks use, and energy consumption using Markov chains. With these models,
different scenarios are evaluated to compare the energy consumption performance of the four
most common timing strategies of instant messaging applications. Thus understand the impact
of adopting these in the development of applications dedicated to health care. Elaborate models
for evaluating the performance of application protocols and the effects of the device’s network
interfaces to preserve the device’s energy resource.
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Nedzingahe, Livhuwani. "Forecasting models for operational and tactical requirements in electricity consumption: The case of the Ferrochrome Sector in South Africa." Thesis, University of Limpopo (Medunsa Campus ), 2010. http://hdl.handle.net/10386/1150.
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Thesis (Mathematics) -- University of Limpopo, 2010<br>Forecasting electricity consumption is a challenge for most power utilities. In South Africa the anxiety posed by electricity supply disruption is a cause for concern in sustainable energy planning. Accurate forecasting of future electricity consumption has been identified as an essential input to this planning process. Forecasting electricity consumption has been widely researched and several methodologies
suggested. However, various methods that have been proposed by a number of researchers are dependent on environment and market factors related to the scope of
work under study making portability a challenge. The aim of this study is to investigate models to forecast short term electricity consumption for operational use
and medium term electricity consumption for tactical use in the Ferrochrome sector in South Africa. An Autoregressive Moving Average method is suggested as an appropriate tool for operational planning. The Holt-Winter Linear seasonal smoothing method is suggested for tactical planning.
Keywords:
Forecasting, electricity consumption, operational planning, tactical
planning, ARIMA, Holt-Winter Linear seasonal smoothing, Ferrochrome sector
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Durak, Tolga. "A Framework for Simplified Residential Energy Consumption Assessment towards Developing Performance Prediction Models for Retrofit Decision-Making." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77255.
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This research proposes to simplify the energy consumption assessment for residential homes while building the foundation towards the development of prediction tools that can achieve a credible level of accuracy for confident decision making. The energy consumption assessment is based on simplified energy consumption models. The energy consumption analysis uses a reduced number of energy model equations utilizing a critical, limited set of parameters. The results of the analysis are used to develop the minimum set of consumption influence parameters with predicted effects for each energy consumption domain.
During this research study, multiple modeling approaches and occupancy scenarios were utilized according to climate conditions in Blacksburg, Virginia. As a part of the analysis process, a parameter study was conducted to: develop a comprehensive set of energy consumption influence parameters, identify the inter-relationships among parameters, determine the impact of energy consumption influence parameters in energy consumption models, and classify energy consumption influence parameters under identified energy consumption domains.
Based on the results of the parameter study, a minimum set of parameters and energy consumption influence matrices were developed. This research suggests the minimum set of parameters with predicted effects to be used during the development of the simplified baseline energy consumption model.<br>Ph. D.
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Moniot, Matthew Louis. "Path Selection to Minimize Energy Consumption of an Electric Vehicle using Synthetic Speed Profiles and Predictive Terminal Energy." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78223.
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Manufacturers of passenger vehicles are experiencing increased pressure from consumers and legislators due to the impact of transportation on the environment. Automotive manufacturers are responding by designing more sustainable forms of transportation through a variety of efforts, including increased vehicle efficiency and the electrification of vehicle powertrains (plug in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV)). An additional method for reducing the environmental impact of personal transport is eco-routing, a methodology which selects routes on the basis of energy consumption.
Standard navigation systems offer route alternatives between a user clarified origin and destination when there are multiple paths available. These alternatives are commonly weighted on the basis of minimizing either total travel time (TTT) or trip distance. Eco-routing offers an alternative criterion – minimizing route energy consumption. Calculation of the energy consumption of a route necessitates the creation of a velocity profile which models how the route will be driven and a powertrain model which relates energy consumption to the constructed velocity profile. Existing research efforts related to both of these aspects typically require complex analysis and proprietary vehicle properties.
A new approach to weighting the energy consumption of different routes is presented within this paper. The process of synthesizing velocity profiles is an improvement upon simpler models while requiring fewer variables as compared to more complex models. A single input, the maximum acceleration, is required to tune driver aggressiveness throughout an entire route. Additionally, powertrain results are simplified through the application of a new parameter, predictive terminal energy. The parameter uses only glider properties as inputs, as compared to dedicated powertrain models which use proprietary vehicle information as inputs which are not readily available from manufacturers. Application of this research reduces computation time and increases the number of vehicles for which this analysis can be applied. An example routing scenario is presented, demonstrating the capability of the velocity synthesis and predictive terminal energy methodologies.<br>Master of Science
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Muendej, Krisanee. "Predictions of monthly energy consumption and annual patterns of energy usage for convenience stores by using multiple and nonlinear regression models." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1221.
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Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models are applied to historical data for two years: 2001 and 2002. The approaches are (1) to analyze nonlinear regression models for long term forecasting of annual patterns compared with outdoor temperature, and (2) to analyze multiple regression models for the building type regardless of outdoor temperature.
In the first approach, twenty four buildings are categorized as base load group and no base group. Average temperature, cooling efficiencies, and cooling knot temperature are estimated by nonlinear regression models: segment and parabola models. The adjusted r-square results in good performance up to ninety percent accuracy. In the second approach, the other selected six buildings are categorized as no trend group. This group does not respond to outdoor temperature. As the result, multiple a regression model is formed by combination of variables from the nonlinear models and physical building variables of cooling efficiency, cooling temperature, light bulbs, area, outdoor temperature, and orientation of fronts. This model explains up to sixty percent of all convenience stores' data.
In conclusion, the accuracy of prediction models is measured by the adjusted r-square results. Among these three models, the multiple regression model shows the highest adjusted r-square (0.597) over the parabola (0.5419) and segment models (0.4806). When the three models come to the application, the multiple regression model is best fit for no trend data type. However, when it is used to predict the energy consumption with the buildings that relate to outdoor temperature, segment and parabola model provide a better prediction result.
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Donovan, James George. "Fracture Toughness Based Models for the Prediction of Power Consumption, Product Size, and Capacity of Jaw Crushers." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/28544.
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There is little process control employed at aggregate crushing plants and essentially no optimization at the primary or jaw crushing stage. Jaw crusher selection is very dependent on the subjective judgment/experience of individuals, the characterization of rock material using inadequate and unrepresentative tests, and the desire to limit secondary breakage, resulting in the conservative selection and operation of jaw crushers. A method for predicting the power consumption, product size, and volumetric capacity of jaw crushers based on fracture toughness has been proposed in this study. A new fracture toughness test, the Edge Notched Disk Wedge Splitting test, has been developed and verified in order to rapidly assess the fracture toughness of six quarry rocks. A High Energy Crushing Test system has been used to simulate the operational settings of a jaw crusher so that comparison of fracture toughness, specific comminution energy, and breakage distribution could be performed. The results indicate that the specific comminution energy required to reduce a rock particle to a given size increases with fracture toughness. The breakage distribution has also been shown to be dependent upon fracture toughness as long as the elastic modulus is taken into account. Laboratory jaw crushing experiments show that the capacity of a jaw crusher is dependent upon fracture toughness and the elastic modulus. Models for the prediction of power consumption, breakage function/product size, and volumetric capacity have been developed based on these results. At the experimental level, the models were able to predict the specific comminution energy to within 1% and t10 (characteristic crushing parameter) to within 10%. Prediction of the product size distribution produced by a lab-scale jaw crusher, for four different rocks, was within ± 5% (in terms of percent passing). The models allow for the selection of a jaw crusher based on the nature of the rock being broken and the average amount of size reduction done on the feed material. The models can also be used to optimize feed and operational settings, as well to determine the product size produced for a given rock and reduction ratio.<br>Ph. D.
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Anasis, John George. "A Combined Energy and Geoengineering Optimization Model (CEAGOM) for Climate Policy Analysis." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2620.
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One of the greatest challenges that will face humanity in the 21st century is the issue of climate change brought about by emissions of greenhouse gases. Energy use is one of the primary sources of greenhouse gas emissions. However, it is also one of the most important contributors to improved human welfare over the past two centuries and will continue to be so for years to come. This quandary has led a number of researchers to suggest that geoengineering may be required in order to allow for continued use of fossil fuels while at the same time mitigating the effects of the associated greenhouse gas emissions on the global climate. The goal of this research was to develop a model that would allow decision-makers and policy analysts to assess the optimal mix of energy and geoengineering resources needed to meet global or regional energy demand at the lowest cost while accounting for appropriate emissions, greenhouse gas concentration, or temperature rise constraints. The resulting software model is called the Combined Energy and Geoengineering Optimization Model (CEAGOM). CEAGOM was then used to analyze the recently announced U.S.-China emissions agreement and to assess what the optimal global energy resource mix might be over the course of the 21st century, including the associated potential need for geoengineering. These analyses yielded optimal mixes of energy and geoengineering resources that could be used to inform regional and global energy and climate management strategies.
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Stuart, Graeme. "Monitoring energy performance in local authority buildings." Thesis, De Montfort University, 2011. http://hdl.handle.net/2086/4964.
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Energy management has been an important function of organisations since the oil crisis of the mid 1970’s led to hugely increased costs of energy. Although the financial costs of energy are still important, the growing recognition of the environmental costs of fossil-fuel energy is becoming more important. Legislation is also a key driver. The UK has set an ambitious greenhouse gas (GHG) reduction target of 80% of 1990 levels by 2050 in response to a strong international commitment to reduce GHG emissions globally. This work is concerned with the management of energy consumption in buildings through the analysis of energy consumption data. Buildings are a key source of emissions with a wide range of energy-consuming equipment, such as photocopiers or refrigerators, boilers, air-conditioning plant and lighting, delivering services to the building occupants. Energy wastage can be identified through an understanding of consumption patterns and in particular, of changes in these patterns over time. Changes in consumption patterns may have any number of causes; a fault in heating controls; a boiler or lighting replacement scheme; or a change in working practice entirely unrelated to energy management. Standard data analysis techniques such as degree-day modelling and CUSUM provide a means to measure and monitor consumption patterns. These techniques were designed for use with monthly billing data. Modern energy metering systems automatically generate data at half-hourly or better resolution. Standard techniques are not designed to capture the detailed information contained in this comparatively high-resolution data. The introduction of automated metering also introduces the need for automated analysis. This work assumes that consumption patterns are generally consistent in the short-term but will inevitably change. A novel statistical method is developed which builds automated event detection into a novel consumption modelling algorithm. Understanding these changes to consumption patterns is critical to energy management. Leicester City Council has provided half-hourly data from over 300 buildings covering up to seven years of consumption (a total of nearly 50 million meter readings). Automatic event detection pinpoints and quantifies over 5,000 statistically significant events in the Leicester dataset. It is shown that the total impact of these events is a decrease in overall consumption. Viewing consumption patterns in this way allows for a new, event-oriented approach to energy management where large datasets are automatically and rapidly analysed to produce summary meta-data describing their salient features. These event-oriented meta-data can be used to navigate the raw data event by event and are highly complementary to strategic energy management.
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Massana, i. Raurich Joaquim. "Data-driven models for building energy efficiency monitoring." Doctoral thesis, Universitat de Girona, 2018. http://hdl.handle.net/10803/482148.
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Nowadays, energy is absolutely necessary all over the world. Taking into account the advantages that it presents in transport and the needs of homes and industry, energy is transformed into electricity.
Bearing in mind the expansion of electricity, initiatives like Horizon 2020, pursue
the objective of a more sustainable future: reducing the emissions of carbon and electricity consumption and increasing the use of renewable energies. As an answer to the shortcomings of the traditional electrical network, such as large distances to the point of consumption, low levels of flexibility, low sustainability, low quality of energy, the difficulties of storing electricity, etc., Smart Grids (SG), a natural evolution of the classical network, has appeared.
One of the main components that will allow the SG to improve the traditional grid is the Energy Management System (EMS). The EMS is necessary to carry out the management of the power network system, and one of the main needs of the EMS is a prediction system: that is, to know in advance the electricity consumption. Besides, the utilities will also require predictions to manage the generation, maintenance and their investments. Therefore, it is necessary to dispose of the systems of prediction of the electrical consumption that, based on the available data, forecast the consumption of the next hours, days or months, in the most accurate way possible.
It is in this field where the present research is placed since, due to the proliferation of sensor networks and more powerful computers, more precise prediction systems have been developed.
Having said that, a complete study has been realized in the first work, taking into account the need to know, in depth, the state of the art, in relation to the load forecasting topic. On the basis of acquired knowledge, the installation of sensor networks, the collection of consumption data and modelling, using Autoregressive (AR) models, were performed in the second work. Once this model was defined, in the third work, another step was made, collecting new data, such as building occupancy, meteorology and indoor ambience, testing several paradigmatic models, such as Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Support Vector Regression (SVR), and establishing which exogenous data improves the prediction accuracy of the models. Reaching this point, and having corroborated that the use of occupancy data improves the prediction, there was the necessity of generating techniques and methodologies, in order to have the occupancy data in advance. Therefore, several attributes of artificial occupancy were designed, in order to perform long-term hourly consumption predictions, in the fourth work.<br>A dia d’avui l’energia és un bé completament necessari arreu del món. Degut als avantatges que presenta en el transport i a les necessitats de les llars i la indústria, l’energia és transformada en energia elèctrica.
Tenint en compte la total expansió i domini de l’electricitat, iniciatives com Horitzó 2020, tenen per objectiu un futur més sostenible: reduint les emissions de carboni i el consum i incrementant l’ús de renovables. Partint dels defectes de la xarxa elèctrica clàssica, com són gran distància al punt de consum, poca flexibilitat, baixa sostenibilitat, baixa qualitat de l’energia, dificultats per a emmagatzemar energia, etc. apareixen les Smart Grid (SG), una evolució natural de la xarxa clàssica.
Un dels principals elements que permetrà a les SG millorar les xarxes clàssiques és l’Energy Management System (EMS). Així doncs, per a que l’EMS pugui dur a terme la gestió dels diversos elements, una de les necessitats bàsiques dels EMS serà un sistema de predicció, o sigui, saber per endavant quin consum hi haurà en un entorn determinat. A més, les empreses subministradores d’electricitat també requeriran de prediccions per a gestionar la generació, el manteniment i fins i tot les inversions a llarg termini. Així doncs ens calen sistemes de predicció del consum elèctric que, partint de les dades disponibles, ens subministrin el consum que hi haurà d’aquí a unes hores, uns dies o uns mesos, de la manera més aproximada possible.
És dins d’aquest camp on s’ubica la recerca que presentem. Degut a la proliferació de xarxes de sensors i computadors més potents, s’han pogut desenvolupar sistemes de predicció més precisos.
A tall de resum, en el primer treball, i tenint en compte que s’havia de conèixer en profunditat l’estat de la qüestió en relació a la predicció del consum elèctric, es va fer una anàlisi completa de l’estat de l’art. Un cop fet això, i partint del coneixement adquirit, en el segon treball es va dur a terme la instal•lació de les xarxes de sensors, la recollida de dades de consum i el modelatge amb models lineals d’auto-regressió (AR). En el tercer treball, un cop fets els models es va anar un pas més enllà recollint dades d’ocupació, de meteorologia i ambient interior, provant diferents models paradigmàtics com Multiple Linear Regression (MLR), Artificial Neural Network (ANN) i Support Vector Regression (SVR) i establint quines dades exògenes milloren la predicció dels models. Arribat a aquest punt, i havent corroborat que l’ús de dades d’ocupació millora la predicció, es van generar tècniques per tal de disposar de les dades d’ocupació per endavant, o sigui a hores vista. D’aquesta manera es van dissenyar diferents atributs d’ocupació artificials, permetent-nos fer prediccions horàries de consum a llarg termini. Aquests conceptes s’expliquen en profunditat al quart treball.
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Spitilli, Ludovico. "Optimization models for optimal battery operation in Swiss energy communities: the Lugaggia case." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25692/.
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The next 30 years are going to be crucial for the future of climate in Europe and in the world: the globally increase of the population and the rise of the energy demand to guarantee higher life-standards, while keeping carbon-emissions low, are pushing the road of the Paris-agreement goals towards challenging paths.
A massive and never-seen before green revolution is nedeed as well as the commitment of all the policy-makers involved to be on track with these goals.
Europe can be considered the pioneer of this green revolution since at the heart of its Climate-Energy strategy, i.e. the Green Deal, there is a large economic investment (1/3 of the 1.8 trillion euros investments from the NextGenerationEU Recovery Plan and the EU’s seven-year budget) and a more ambitious program in the short term like an improvement of 32.5% in energy efficiency and a 32% share for Renewable Energy by 2030 .
The legislative acts to reach these targets are contained in the Clean Energy Package. It can be considered the EU's "energy rulebook" and it's based on 3 key pillars: energy efficiency, renewable energy generation and the new role of consumers in a greener society.
In this contest, there is a legislative support for collective energy actions and citizens’ active participation in energy markets through the definition of Energy Communities.
This thesis aims to describe the role of Energy Communities in Europe with a particular focus to Switzerland.
In chapter 1 the regulatory framework will be introduced, followed in chapter 2 by the different business models that can be implemented. In the 3rd chapter, the Lugaggia energy community, object of study of this thesis, is presented. After a discussion on convex optimization theory in chapter 4, the algorithms used to reach an optimal economical operation of the energy community are introduced in chapter 5. In the end, chapter 6, the results are analyzed and discussed.
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Silva, Rafael Rodrigues da 1980. "Modelos para análises de sistemas energéticos industriais aplicados a estudos de eficiência energética." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264458.
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Orientador: Sergio Valdir Bajay<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-22T09:13:39Z (GMT). No. of bitstreams: 1
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Previous issue date: 2013<br>Resumo: Sistemas industriais consumidores de energia são, em geral, complexos. Metodologias e ferramentas que facilitem o cálculo de potenciais de economia no consumo de energia térmica e elétrica são, portanto, de extrema importância. Nesse contexto, modelos de simulação de sistemas energéticos são bastante úteis para explicar o comportamento de sistemas complicados, que estão além da capacidade do cérebro humano compreender integralmente todas as suas características relevantes. Esse trabalho apresenta o contexto brasileiro de oportunidades para uso de softwares que se propõem a realizar a modelagem de sistemas energéticos industriais em alguns dos seus principais sistemas consumidores de energia: força motriz, ar comprimido, calor de processo, aquecimento direto, refrigeração e resfriamento, sistemas de bombeamento e sistemas de ventilação. São consideradas duas fontes de disponibilização desse tipo de ferramenta. A primeira é o Programa Nacional de Conservação de Energia Elétrica (Procel), que possui softwares nacionais, em língua portuguesa, para análises de alguns dos sistemas listados anteriormente. A segunda é o U.S. Department of Energy (DOE), pela tradição do país no uso deste tipo de ferramenta com profissionais habilitados formalmente, pela língua inglesa que se torna cada vez mais comum no Brasil e também pela abundância de softwares gratuitos disponibilizados. Comparações quanto às complementaridades ou redundâncias de propostas, plataformas, idiomas, pacotes de unidades e profundidades das análises são efetuadas. Conjuntamente, para aprofundar as análises dos programas, são efetuados estudos de casos reais na indústria nacional com o uso de modelos de simulação. Foram selecionados inicialmente 25 modelos de simulação. Após a eliminação de modelos que faziam análises semelhantes, ou que apresentavam grandes dificuldades para a sua instalação, ou operação, sobraram 17 modelos, que possibilitam boas análises do desempenho energético de sistemas industriais envolvendo, sobretudo, motores elétricos, ar comprimido, calor de processo e aquecimento direto. A maior parte dos sistemas industriais com considerável potencial de economia de energia pode ser analisada por, pelo menos, um destes modelos. Nesse ambiente de modelos e simulações, também é apresentado um estudo sobre a aplicação de modelagem de otimização (matemática) para um sistema hipotético de ar comprimido<br>Abstract: In general, industrial energy systems are complex. Methodologies and tools which make easier the calculation of potential thermal and electric energy savings are therefore of utmost importance. Within this context, simulation models of energy systems are very useful in explaining the behavior of complicated systems, which are beyond the capacity of the human brain to fully understand all of their relevant features. This work presents the Brazilian context of opportunities in terms of software usage for modeling some of the major industrial energy systems: motors, compressed air, steam, process heating, process cooling, pumping systems, and ventilation systems. Two sources of this kind of software tool are considered. The first one is the Brazilian National Program for Energy Conservation (Procel), which has some national software, in Portuguese, capable of analyzing some of the energy systems previously listed. The second one is the U.S. Department of Energy (DOE), due to the country's tradition in using this kind of tools with recognized qualified specialists, the English language that is becoming increasingly present in Brazil and the abundance of free of charge software tools in the DOE's website. Comparisons are made regarding: redundancies and complementarities of the proposals, platform structures, languages, units and complexity of analysis. Additionally, real case studies in the Brazilian industry are made using simulation models. Initially, 25 simulation models were selected. After the elimination of models performing similar analyses, or being very difficult to install or operate, 17 models remained, which allow good energy performance analyses of industrial systems, particularly those comprising electric motors, compressed air, steam and process heating. Most of the industrial systems with large potential for energy savings can be evaluated by, at least, one of these models. A study about the application of mathematical optimization modeling for a hypothetical compressed air system is also presented<br>Mestrado<br>Planejamento de Sistemas Energeticos<br>Mestre em Planejamento de Sistemas Energéticos
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Stage, Jesper. "Mixing oil and water : studies of the Namibian economy /." Umeå : Univ, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-117.
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Warpe, Hrusheekesh Sunil. "An Analysis of EcoRouting Using a Variable Acceleration Rate Synthesis Model." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78678.
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Automotive manufacturers are facing increasing pressure from legislative bodies and consumers to reduce fuel consumption and greenhouse gas emissions of vehicles. This has led to many automotive manufacturers starting production of Plug-in Hybrid Electric Vehicles (PHEV's) and Battery Electric Vehicles (BEV's). Another method that helps to reduce the environmental effect of transportation is EcoRouting.
The standard Global Positioning System (GPS) navigation offers route alternatives between user specified origin and destination. This technology provides multiple routes to the user and focuses on reducing the travel time to reach to the destination. EcoRouting is the method to determine a route that minimizes vehicle energy consumption, unlike traditional routing methods that minimize travel time. An EcoRouting system has been developed as a part of this thesis that takes in information such as speed limits, the number of stop lights, and the road grade to calculate the energy consumption of a vehicle along a route.
A synthesis methodology is introduced that takes into consideration the distance between the origin and destination, the acceleration rate of the vehicle, cruise speed and jerk rate as inputs to simulate driver behavior on a given route. A new approach is presented in this thesis that weighs the energy consumption for different routes and chooses the route with the least energy consumption, subject to a constraint on travel time. A cost function for quantifying the effect of travel time is introduced that assists in choosing the EcoRoute with an acceptable limit on the travel time required to reach the destination.
The analysis of the EcoRouting system with minimum number of conditional stops and maximum number of conditional stops is done in this thesis. The effect on energy consumption with the presence and absence of road-grade information along a route is also studied. A sensitivity study is performed to observe the change in energy consumption of the vehicle with a change in acceleration rates and road grade. Three routing scenarios are presented in this thesis to demonstrate the functionality of EcoRouting. The EcoRouting model presented in this thesis is also validated against an external EcoRouting research paper and the energy consumption along three routes is calculated. The EcoRoute solution is found to vary with the information given to the variable acceleration rate model. The synthesis and the results that are obtained show that parameters such as acceleration, deceleration, and road grade affect the overall energy consumption of a vehicle and are helpful in determining the EcoRoute.<br>Master of Science
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Yen, Jeffrey Lee. "A system model for assessing water consumption across transportation modes in urban mobility networks." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39527.
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Energy and environmental impacts are two factors that will influence urban region composition in the near future. One emerging issue is the effect on water usage resulting from changes in regional or urban transportation trends. With many regions experiencing stresses on water availability, transportation planners and users need to combine information on transportation-related water consumption for any region and assess potential impacts on local water resources from the expansion of alternative transportation modes. This thesis will focus on use-phase water consumption factors for multiple vehicle modes, energy and fuel pathways, roads, and vehicle infrastructure for a given transportation network.
While there are studies examining life cycle impacts for energy generation and vehicle usage, few repeatable models exist for assessing overall water consumption across several transportation modes within urban regions. As such, the question is: is it possible to develop a traceable decision support model that combines and assesses water consumption from transportation modes and related mobility infrastructure for a given mobility network? Based on this, an object-oriented system model of transportation elements was developed using the Systems Modeling Language (SysML) and Model-Based Systems Engineering principles to compare water consumption across vehicle modes for assessing the resiliency of existing infrastructure and water resources.
To demonstrate the intent of this model, daily network usage water consumption will be analyzed for current and alternative network scenarios projected by policies regarding the expansion of alternative energy. The model is expected to show variations in water consumption due to fluctuations in energy pathways, market shares, and driving conditions, from which the model should help determine the feasibility of expanding alterative vehicles and fuels in these networks. While spatially explicit data is limited compared to the national averages that are used as model inputs, the analytical framework within this model closely follows that of existing assessments and the reusable nature of SysML model elements allows for the future expansion of additional transportation modes and infrastructure as well as other environmental analyses.
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Andrade, Diogo Elias da Vinha. "Modelo para predição de resultados de ensaios de sistemas de refrigeração em tempo real." Universidade Tecnológica Federal do Paraná, 2012. http://repositorio.utfpr.edu.br/jspui/handle/1/207.
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Atualmente, a análise de desempenho de sistemas de refrigeração domésticos é realizada através de ensaios experimentais normalizados. Durante esses experimentos, diversas variáveis como pressões de trabalho, temperaturas em diversos pontos do sistema, corrente elétrica e potência consumida, são monitoradas. Porém, em muitos casos são necessárias mais de 24 horas para execução de um teste experimental (e.g., teste abaixamento de temperatura). Tendo em vista o tempo despendido nestes testes, propõe-se no presente trabalho um modelo matemático semi-empírico capaz de predizer o comportamento das variáveis do sistema testado e, com isso, antecipar o final do ensaio. O modelo, desenvolvido através das leis de conservação da massa e da energia, apresenta parâmetros que são ajustados a partir de informações experimentais obtidas durante a execução do próprio teste. Após a inicialização do ensaio, a cada período de tempo prédeterminado, os dados medidos são utilizados para determinar os parâmetros empíricos do modelo. Obtidas as constantes, simula-se o comportamento das principais variáveis do sistema de refrigeração até a condição de regime permanente. Com isso, o teste experimental pode ser finalizado com antecedência. O modelo desenvolvido é capaz de prever com boa precisão, a partir de duas horas de teste, a variação da vazão mássica e da pressão de sucção (com diferenças da ordem de 10% em regime permanente quando comparadas às variáveis experimentais), da pressão de condensação (com diferença da ordem de 5%) e da temperatura da parede do condensador (diferença da ordem de 2°C).<br>The performance of household refrigeration systems are usually evaluated through experimental tests carried at in temperature and humidity controlled chambers. During the tests, the discharge and suction pressures, the temperature in several system positions, and the compressor power are measured. These tests are expensive and time-demanding, e.g., a single pull-down test can take more than 24 hours to be performed. Although the mathematical models have been proposed for decades as an alternative to the experiments, they are not sufficiently reliable to substitute completely the tests. Therefore, the current work proposes a semiempirical mathematical model to predict the system performance with the purpose of reducing the test time instead of replacing it. The model is based on the mass and energy conservation equations in which the constant parameters, such as conductance and capacitances, are calibrated from previous measured values of temperature and pressure. As soon as the parameters are obtained, a simulation is performed to forecast future values of temperature, pressure and compressor power and therefore, to anticipate the end of the test. Calibrations and simulations can be continuously performed as the test evolves. Preliminary results show that steadystate values of discharge and suction pressures can be predicted within error bands of 5 and 10%, respectively, after only two hours of a pull-down test being performed.
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Cattin, Johana. "Consideration of dynamic traffic conditions in the estimation of industrial vehicules energy consumption while integrating driving assistance strategies." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSET003/document.
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Le monde industriel, et en particulier l’industrie automobile, cherche à représenter au mieux le réel pour concevoir des outils et produits les plus adaptés aux enjeux et marchés actuels. Dans cette optique, le groupe Volvo a développé de puissants outils pour la simulation de la dynamique des véhicules industriels. Ces outils permettent notamment l’optimisation de composants véhicules ou de stratégies de contrôle. De nombreuses activités de recherche portent sur des technologies innovantes permettant de réduire la consommation des véhicules industriels et d’accroitre la sécurité de leurs usages dans différents environnements. En particulier, le développement des systèmes d’aide à la conduite automobile ITS et ADAS. Afin de pouvoir développer ces systèmes, un environnement de simulation permettant de prendre en compte les différents facteurs pouvant influencer la conduite d’un véhicule doit être mis en place. L’étude se concentre sur la simulation de l’environnement du véhicule et des interactions entre le véhicule et son environnement direct, i.e. le véhicule qui le précède. Les interactions entre le véhicule étudié et le véhicule qui le précède sont modélisées à l’aide de modèles mathématiques, nommés lois de poursuites. De nombreux modèles existent dans la littérature mais peu concernent le comportement des véhicules industriels. Une étude détaillée de ces modèles et des méthodes de calage est réalisée. L’environnement du véhicule peut être représenté par deux catégories de paramètres : statiques (intersections, nombre de voies…) et dynamiques (état du réseau). A partir d’une base de données de trajets usuels, ces paramètres sont calculés, puis utilisés pour générer de manière automatisée des scénarios de simulation réalistes<br>The industrial world, and in particular the automotive industry, is seeking to best represent the real world in order to design tools and products that are best adapted to current challenges and markets, by reducing development times and prototyping costs. With this in mind, the Volvo Group has developed powerful tools to simulate the dynamics of industrial vehicles. These tools allow the optimization of vehicle components or control strategies. Many research activities focus on innovative technologies to reduce the consumption of industrial vehicles and increase the safety of their use in different environments. Particularly, the development of ITS and ADAS is booming. In order to be able to develop these systems, a simulation environment must be set up to take into account the various factors that can influence the driving of a vehicle. The work focuses on simulating the vehicle environment and the interactions between the vehicle and its direct environment, i.e. the vehicle in front of it. The interactions between the vehicle under study and the vehicle in front of it are modelled using mathematical models, called car-following models. Many models exist in the literature, but few of them deals specifically with heavy duty vehicles. A specific focus on these models and their calibration is realized. The vehicle environment can be represented by two categories of parameters: static (intersections, number of lanes) and dynamic parameters (state of the network). From a database of usuals roads, these parameters are computed, then, they are used to automatically generate realist traffic simulation scenarios
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Klockar, Zack. "Development of grey-box models for simulating heating consumption in buildings : A study applying system identification methodology to a physics-based framework." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448097.
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This study models the energy used for heating in buildings by applying system identification methodology. The model development is grounded in physics to provide guidance and interpretability when evaluated. Time-series of heating demand, outdoor temperature, indoor temperature and solar irradiation are considered for the modelling purpose. Evaluation is done through simulation and relies on quantitative measures, residual analysis and visual inspection of model output. Through model development, the study seeks to extrapolate information of physical properties that drives heating demand in buildings. Seven buildings located in the same geographic area are studied. It is found that linear ARX-models can simulate heating demand with high precision but at times low accuracy. A common system model structure can be identified between buildings, indicating that physical properties shared between buildings can be identified through this methodology. A sensitivity analysis is conducted to derive the contributions from model constituents to simulation results. Two buildings were also modelled as OE-models. These models performed better than the respective ARX-models but were deemed more difficult to use for the purpose of this study. The study finds difficulties in implementing aggregated time-series of indoor temperature, which could be explored further in future studies for more detailed interpretations.
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MENDONÇA, NETO Júlio Rodrigues de. "Modelagem e análise de desempenho e consumo de energia em aplicações móveis." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/15901.
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Previous issue date: 2015-08-03<br>FACEPE<br>É notável a propagação de dispositivos móveis inteligentes em todo o mundo. Em 2016,
espera-se que o número de smartphones supere os 2 bilhões. A entrada desses aparelhos no mercado têm mudado o estilo de vida das pessoas, oferecendo soluções com mais facilidade e praticidade, como por exemplo, a realização de transações bancárias. A variedade e facilidade de acesso aos serviços oferecidos pelos mesmos têm ajudado nesta mudança. Além disso, o número de aplicativos nos marketplaces para satisfazer as mais diferentes necessidades dos usuários tem aumentado bastante. Por estes motivos, as aplicações móveis têm ganhado cada vez mais destaque na indústria de Tecnologia da Informação e Comunicação (TIC). Contudo, apesar da
evolução da computação móvel na última década, a tecnologia das baterias desses dispositivos não evoluíram na mesma velocidade. Sendo, portanto, o curto tempo de vida das baterias uma das maiores preocupações entre fabricantes de hardware e software para dispositivos móveis. É comum o uso de modelos analíticos para analisar o comportamento dos sistemas. Modelos formais como cadeias de Markov e redes de Petri são bastante utilizadas com esse propósito. Estes tipos de modelos tem fundamentação matemática sólida, e portanto, são eficientes para realização de análises quantitativas e verificação de propriedades dos sistemas representados. Entretanto, sua construção requer conhecimento especializado por parte dos projetistas de sistemas. Por outro lado, modelos semiformais, como System Modelling Language (SysML) e Unified Model Language (UML), possuem uma notação mais flexível e são bastante difundidos
no mercado. Contudo, esses modelos semiformais, por não possuírem uma fundamentação formal rígida, não oferecem suporte para o desenvolvimento de técnicas para análises numéricas e verificações de propriedades. Dessa forma, torna-se interessante a combinação do uso de modelos formais e modelos semiformais. Nesse contexto, este trabalho propõe uma abordagem para avaliação de métricas de desempenho, consumo de energia e disponibilidade de aplicações móveis utilizando modelos estocásticos. Para facilitar a construção destes modelos, a abordagem proposta adota a criação de regras de mapeamento, para obtenção de Redes de Petri stocásticas
e Determinísticas (DSPNs) a partir de diagramas da SysML. Desta forma, projetistas que
possuem pouco conhecimento em modelagem estocástica podem realizar análises relacionadas ao desempenho e consumo de energia de aplicações móveis de forma rápida e menos custosa que o desenvolvimento de protótipos, ainda na fase de planejamento do projeto. Por fim, são apresentados estudos de casos que demonstram a aplicabilidade da abordagem.<br>It is remarkable smart mobile devices spread around the world. In 2016, it is expected
that the number of smartphones exceeds 2 billion. These devices entrace on the market have changed the people lifestyle, offering solutions with more ease and practicality, for example, conducting banking transactions. The variety and easy access to services offered by them have helped this change. Also, the number of applications in the marketplaces to meet the many different users needs has greatly increased. For these reasons, mobile applications have gained more prominence in the Information and Communication Technology (ICT) industry. However, despite the mobile computing progress in the last decade, the battery technology of these devices have not evolved at the same speed. It is, therefore, the short lifetime of batteries a major concern between hardware and software manufacturers to mobile devices. Analytical models are usually
used to analyze the systems behavior. Formal models as Markov chains and Petri nets are widely used for this purpose. These types of models have a solid mathematical basis, and therefore, are effective in carrying out quantitative analyzes and verification systems properties. Nevertheless, their construction requires specialized knowledge by the system designers. Moreover, semiformal models, such as System Modelling Language (SysML) and Unified Model Language (UML) have a more flexible notation and are fairly widespread in the market. Meanwhile, these semi-formal models, for not having a rigid formal basis, do not support techniques for numerical analysis and property checks. Therefore, it becomes interesting to use the combination of formal and semi-formal models. In this context, this work proposes an approach to evaluation performance, power consumption and availability metrics of mobile applications using stochastic
models. In order to facilitate construction of such models, the proposed approach adopts the creation of mapping rules for obtaining Stochastic Petri Nets and deterministic (DSPNs) from SysML diagrams. In this way, designers who have little knowledge in stochastic modeling can perform analyzes related to the performance and power consumption in mobile applications quickly and less costly to develop prototypes, still in the project planning phase. Finally, case studies are presented to demonstrate the approach applicability.
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Fachrizal, Reza. "Synergy between Residential Electric Vehicle Charging and Photovoltaic Power Generation through Smart Charging Schemes : Models for Self-Consumption and Hosting Capacity Assessments." Licentiate thesis, Uppsala universitet, Byggteknik och byggd miljö, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-419665.
Full textAbstract:
The world is now in a transition towards a more sustainable future. Actions to reduce the green-house gases (GHG) emissions have been promoted and implemented globally, including switching to electric vehicles (EVs) and renewable energy technologies, such as solar photovoltaics (PV). This has led to a massive increase of EVs and PV adoption worldwide in the recent decade. However, large integration of EVs and PV in buildings and electricity distribution systems pose new challenges such as increased peak loads, power mismatch, component overloading, and voltage violations, etc. Improved synergy between EVs, PV and other building electricity load can overcome these challenges. Coordinated charging of EVs, or so-called EV smart charging, is believed to a promising solution to improve the synergy. This licentiate thesis investigates the synergy between residential EV charging and PV generation with the application of EV smart charging schemes. The investigation in this thesis was carried out on the individual building, community and distribution grid levels. Smart charging models with an objective to reduce the net-load (load - generation) variability in residential buildings were developed and simulated. Reducing the net-load variability implies both reducing the peak loads and increasing the self-consumption of local generation, which will also lead to improved power grid performance. Combined PV-EV grid hosting capacity was also assessed. Results show that smart charging schemes could improve the PV self-consumption and reduce the peak loads in buildings with EVs and PV systems. The PV self-consumption could be increased up to 8.7% and the peak load could be reduced down to 50%. The limited improvement on self-consumption was due to low EV availability at homes during midday when the solar power peaks. Results also show that EV smart charging could improve the grid performance such as reduce the grid losses and voltage violation occurrences. The smart charging schemes improve the grid hosting capacity for EVs significantly and for PV slightly. It can also be concluded that there was a slight positive correlation between PV and EV hosting capacity in the case of residential electricity distribution grids.
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Sandels, Claes. "Modeling and Simulation of Electricity Consumption Profiles in the Northern European Building Stock." Doctoral thesis, KTH, Elkraftteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-184093.
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The electric power systems are currently being transformed through the integration of intermittent renewable energy resources and new types of electric loads. These developments run the risk of increasing mismatches between electricity supply and demand, and may cause non-favorable utilization rates of some power system components. Using Demand Response (DR) from flexible loads in the building stock is a promising solution to overcome these challenges for electricity market actors. However, as DR is not used at a large scale today, there are validity concerns regarding its cost-benefit and reliability when compared to traditional investment options in the power sector, e.g. network refurbishment. To analyze the potential in DR solutions, bottom-up simulation models which capture consumption processes in buildings is an alternative. These models must be simple enough to allow aggregations of buildings to be instantiated and at the same time intricate enough to include variations in individual behaviors of end-users. This is done so the electricity market actor can analyze how large volumes of flexibility acts in various market and power system operation contexts, but also can appreciate how individual end-users are affected by DR actions in terms of cost and comfort. The contribution of this thesis is bottom-up simulation models for generating load profiles in detached houses and office buildings. The models connect end-user behavior with the usage of appliances and hot water loads through non-homogenous Markov chains, along with physical modeling of the indoor environment and consumption of heating and cooling loads through lumped capacitance models. The modeling is based on a simplified approach where openly available data and statistics are used, i.e. data that is subject to privacy limitations, such as smart meter measurements are excluded. The models have been validated using real load data from detached houses and office buildings, related models in literature, along with energy-use statistics from national databases. The validation shows that the modeling approach is sound and can provide reasonably accurate load profiles as the error results are in alignment with related models from other research groups. This thesis is a composite thesis of five papers. Paper 1 presents a bottom-up simulation model to generate load profiles from space heating, hot water and appliances in detached houses. Paper 2 presents a data analytic framework for analyzing electricity-use from heating ventilation and air conditioning (HVAC) loads and appliance loads in an office building. Paper 3 presents a non-homogeneous Markov chain model to simulate representative occupancy profiles in single office rooms. Paper 4 utilizes the results in paper 2 and 3 to describe a bottom-up simulation model that generates load profiles in office buildings including HVAC loads and appliances. Paper 5 uses the model in paper 1 to analyze the technical feasibility of using DR to solve congestion problems in a distribution grid.<br>Integrering av förnybara energikällor och nya typer av laster i de elektriska energisystemen är möjliga svar till klimatförändringar och uttömning av ändliga naturresurser. Denna integration kan dock öka obalanserna mellan utbud och efterfrågan av elektricitet, och orsaka en ogynnsam utnyttjandegrad av vissa kraftsystemkomponenter. Att använda efterfrågeflexibilitet (Demand Response) i byggnadsbeståndet är en möjlig lösning till dessa problem för olika elmarknadsaktörer. Men eftersom efterfrågeflexibilitet inte används i stor skala idag finns det obesvarade frågor gällande lösningens kostnadsnytta och tillförlitlighet jämfört med traditionella investeringsalternativ i kraftsektorn. För att analysera efterfrågeflexibilitetslösningar är botten-upp-simuleringsmodeller som fångar elförbrukningsprocesser i byggnaderna ett alternativ. Dessa modeller måste vara enkla nog för att kunna representera aggregeringar av många byggnader men samtidigt tillräckligt komplicerade för att kunna inkludera unika slutanvändarbeteenden. Detta är nödvändigt när elmarknadsaktören vill analysera hur stora volymer efterfrågeflexibilitet påverkar elmarknaden och kraftsystemen, men samtidigt förstå hur styrningen inverkar på den enskilda slutanvändaren. Bidraget från denna avhandling är botten-upp-simuleringsmodeller för generering av elförbrukningsprofiler i småhus och kontorsbyggnader. Modellerna kopplar slutanvändarbeteende med elförbrukning från apparater och varmvattenanvändning tillsammans med fysikaliska modeller av värmedynamiken i byggnaderna. Modellerna är byggda på en förenklad approach som använder öppen data och statistisk, där data som har integritetsproblem har exkluderats. Simuleringsresultat har validerats mot elförbrukningsdata från småhus och kontorsbyggnader, relaterade modeller från andra forskargrupper samt energistatistik från nationella databaser. Valideringen visar att modellerna kan generera elförbrukningsprofiler med rimlig noggrannhet. Denna avhandling är en sammanläggningsavhandling bestående av fem artiklar. Artikel 1 presenterar botten-upp-simuleringsmodellen för genereringen av elförbrukningsprofiler från uppvärmning, varmvatten och apparater i småhus. Artikel 2 presenterar ett dataanalytiskt ramverk för analys av elanvändningen från uppvärmning, ventilation, och luftkonditioneringslaster (HVAC) och apparatlaster i en kontorsbyggnad. Artikel 3 presenterar en icke-homogen Markovkedjemodell för simulering av representativa närvaroprofiler i enskilda kontorsrum. Artikel 4 använder resultaten i artiklarna 2 och 3 för att beskriva en botten-upp-simuleringsmodell för generering av elförbrukningsprofiler från HVAC-laster och apparater i kontorsbyggnader. Artikel 5 använder modellen i artikel 1 för att analysera den tekniska möjligheten att använda efterfrågeflexibilitet för att lösa överbelastningsproblem i ett eldistributionsnät.<br><p>QC 20160329</p>
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Tran, Martina. "Energy Consumption Optimizations for 5G networks." Thesis, Uppsala universitet, Signaler och System, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395146.
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The importance of energy efficiency has grown alongside awareness of climate change due to the rapid increase of greenhouse gases. With the increasing trend regarding mobile subscribers, it is necessary to prevent an expansion of energy consumption via mobile networks. In this thesis, the energy optimization of the new radio access technology called 5G NR utilizing different sleep states to put base stations to sleep when they are not transmitting data is discussed. Energy savings and file latency with heterogeneous and super dense urban scenarios was evaluated through simulations with different network deployments. An updated power model has been proposed and the sensitivity of the new power model was analyzed by adjusting wake-up time and sleep factors. This showed that careful implementation is necessary when adjusting these parameter settings, although in most cases it did not change the end results by much. Since 5G NR has more potential in energy optimization compared to the previous generation mobile network 4G LTE, up to 4 sleep states was implemented on the NR base stations and one idle mode on LTE base stations. To mitigate unnecessary sleep, deactivation timers are used which decides when to put base stations to sleep. Without deactivation timers, the delay could increase significantly, while with deactivation timers the delay increase would only be a few percent. Up to 42.5% energy could be saved with LTE-NR non-standalone deployment and 72.7% energy with NR standalone deployment compared to LTE standalone deployment, while minimally impacting the delay on file by 1%.
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Bae, Kyungcho. "Energy consumption forecasting: Econometric model vs state space model." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/187010.
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This study examines the forecasting performance of two major multivariate methodologies: econometric modeling and multivariate state space modeling. The same variables are used in both models to facilitate comparison. They are evaluated by both expost and exante accuracy of U.S. energy consumption forecasts. Econometric models are highly simplified and a model selection procedure is applied to the models. Two different formats of multivariate state space models are examined: economic structure and identity structure. Goodrich's algorithm is employed to estimate the state space models. The state space models in both the econometric structure and the identity structure provided generally good estimates, usually, but not always, these forecasts were more accurate than those by the single econometric models.
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Marc, Sylvain. "Étude expérimentale et numérique d’un procédé de cuisson par contact direct." Thesis, Lorient, 2017. http://www.theses.fr/2017LORIS473/document.
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La cuisson par contact direct est un mode de préparation des aliments très courant à travers le monde, cependant peu d’études s’intéressent à cette problématique à l’échelle domestique. Ces travaux tentent d’apporter une contribution à l’étude des phénomènes mis en jeu durant cette opération. Ce manuscrit débute par une revue de différents facteurs impliqués lors de la cuisson : les consommations d’énergie, les types d’appareils utilisés, les phénomènes physico-chimiques intervenant dans le produit ou les problématiques de modélisation y sont abordés. Il s’en dégage qu’une donnée essentielle est la connaissance du flux de chaleur transmis au produit. Une méthode d’estimation de ce flux basée sur les techniques inverses est développée. Celle-ci a contribué à concevoir un prototype instrumenté permettant de suivre les cinétiques des températures dans la plaque chauffante et dans un élastomère simulant un produit alimentaire. Les résultats obtenus montrent que la méthode permet d’estimer le flux de chaleur transmis avec une bonne précision. Dans un second temps, une étude expérimentale de la cuisson d’une pâte céréalière d’environ 8mm d’épaisseur est présentée. Après avoir caractérisé les propriétés thermophysiques et hydriques, le prototype est utilisé pour suivre les évolutions de différents paramètres comme les températures, le flux de chaleur, la masse en dynamique, et les teneurs en eau. La répétabilité et la variabilité des résultats suivant la température initiale de la plaque sont menés. Ensuite, un modèle 1D simulant les transferts de matière et d’énergie, est utilisé pour étudier les différents facteurs intervenant lors de la cuisson. Un second modèle 2D est réalisé permettant de tester les consommations d’énergie lors d’une opération de cuisson en cadence suivant différents scénarios de conception du prototype<br>Direct contact cooking is a very common way of preparing foods throughout the world, but few studies are interested in this issue at the domestic scale. This work attempts to contribute to the study of the phenomena involved during this operation. This thesis begins with a review of the various factors involved in the cooking process: energy consumption, types of appliances used, physico-chemical phenomena implied in the product or modeling problems are discussed. It emerges from this that an essential fact is the knowledge of the heat flux transmitted to the product. A method for estimating this flux based on inverse techniques is developed. This has contributed to design an instrumented prototype allowing to follow the kinetics of the temperatures in the heating plate and in an elastomer simulating a food product. The results obtained show that the method makes it possible to estimate the heat flux transmitted with a good accuracy. In a second step, an experimental study of cooking of a 8 mm thick cereal batter is presented. After having characterized the thermophysical and hydric properties, the prototype is used to monitor changes in various parameters such as temperatures, heat flux, mass in dynamics, and water contents. The repeatability and the variability of the results according to the initial temperature of the plate are carried out. Then, a 1D model simulating mass and heat transfers is used to study the different factors involved in cooking. A second 2D model is realized to test the energy consumption during a cooking operation in cadence according to different prototype design scenarios
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Fedor, Craig Steven. "Synthesizing Vehicle Cornering Modes for Energy Consumption Analysis." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83556.
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Automotive vehicle manufacturers have been facing increased pressures from legislative bodies and consumers to reduce the fuel consumption and harmful emissions of their newly produced vehicles as a result of new research showing the detrimental effects these emissions have on the environment. These pressures are encouraging manufactures and researchers to invest billions of dollars into the development of new advanced vehicle technologies. Some of these investments have resulted in substantial progress in powertrain technologies that have led to the preliminary adoption of electrified powertrain vehicles. Other areas of research are actively working to reduce the energy consumption of a vehicle, regardless of its powertrain, by influencing driver behavior and by optimizing the way a vehicle travels between an origin and destination. This intelligent vehicle routing is done by analyzing a range of possible routes and selecting the route that consumes the least amount of fuel.
An accurate method for predetermining vehicle energy expenditure along a given route before it is driven is needed to effectively implement intelligent vehicle routing systems. One common method is the generation of a road network-wide database with energy use figures for each section of road. This method requires expensive experimentation trials or network simulation software. Individual-level vehicle predictive energy estimation eliminates the need for costly fuel use generation by utilizing vehicle velocity generation techniques and vehicle powertrain models. Estimation of individual vehicle energy consumption along a route is done by identifying an origin-destination pair, detecting required full-stops along the path, and synthesizing multiple stop-to-stop velocity modes between each set of stops. The resulting velocity profile is paired with a specific vehicle powertrain model to determine fuel consumption. A drawback of this route generation technique is that the vehicle path is assumed to be one-dimensional and lacks inclusion of road curves and their associated velocity changes to maintain passenger comfort.
This thesis evaluates the merit of discounting road curves in predictive vehicle energy consumption analyses and presents a technique for modeling common road corners that require velocity changes to limit passenger discomfort. The resulting corner synthesis method is combined with a validated vehicle powertrain model to complete full route consumption modeling. Two routes, an urban and highway, are modeled and driven to evaluate the accuracy of the full simulation model when compared with on-road data. The results show that corners can largely be ignored during energy consumption analysis for highways. The cornering effects on a vehicle during urban driving, however, should be included in urban route analyses with multiple road curves. Inclusion of the cornering effects during an example urban route analysis decreased the error between the on-road consumption data and the simulation results.<br>Master of Science
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Csereklyei, Zsuzsanna, and Stefan Humer. "Projecting Long-Term Primary Energy Consumption." WU Vienna University of Economics and Business, 2013. http://epub.wu.ac.at/3874/1/wp152.pdf.
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In this paper we use the long-term empirical relationship among primary energy consumption,
real income, physical capital, population and technology, obtained by averaged
panel error correction models, to project the long-term primary energy consumption of 56
countries up to 2100. In forecasting long-term primary energy consumption, we work with four
different Shared Socioeconomic Pathway Scenarios (SSPs) developed for the Intergovernmental
Panel on Climate Change (IPCC) framework, assuming different challenges to adaptation and
mitigation. We find that in all scenarios, China, the United States and India will be the largest
energy consumers, while highly growing countries will also significantly contribute to energy
use. We observe for most scenarios a sharp increase in global energy consumption, followed
by a levelling-out and a decrease towards the second half of the century. The reasons behind
this pattern are not only slower population growth, but also infrastructure saturation and
increased total factor productivity. This means, as countries move towards more knowledge
based societies, and higher energy efficiency, their primary energy usage is likely to decrease as
a result. Global primary energy consumption is expected however to increase significantly in
the coming decades, thus increasing the pressure on policy makers to cope with the questions
of energy security and greenhouse gas mitigation at the same time. (authors' abstract)<br>Series: Department of Economics Working Paper Series
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Gupta, Deepak Prakash. "Energy sensitive machining parameter optimization model." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4406.
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Thesis (M.S.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains ix, 71 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 67-71).
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Manich, Bou Salvador. "Anàlisi de l'energia de transició màxima en circuits combinacionals CMOS." Doctoral thesis, Universitat Politècnica de Catalunya, 1998. http://hdl.handle.net/10803/6361.
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En la dècada actual, l'augment del consum energètic dels circuits integrats està tenint un impacte cada vegada més important en el disseny electrònic. Segons l'informe de la Semiconductor Industry Association de l'any 1997, es preveu que aquest impacte serà encara major en la propera dècada. En la bibliografia existeixen diversos treballs on es relaciona un consumo energètic elevat amb la degradació de les prestacions i la fiabilitat del xip. Per aquesta raó, el consum energètic ha estat incorporat com a un altre paràmetre a tenir en compte en el disseny dels circuits integrats. Es coneix com a energia de transició l'energia consumida per un circuit combinacional CMOS quan es produeix un canvi en les seves entrades. Una energia de transició excessivament elevada pot afectar a la fiabilitat del xip a través dels anomenats hot spots, i de l'electromigració. Altres efectes com el ground bouncing i la signal integrity degradation poden repercutir en les prestacions del circuit. La minimització de les degradacions esmentades anteriorment requereixen de la caracterització de l'energia de transició màxima durant la fase de disseny. A tal efecte, en aquesta tesi es proposen dues metodologies que permeten l'estimació de l'energia de transició màxima en circuits combinacionals CMOS. Donat que l'estimació del nivell màxim exacte es inviable en circuits a partir de mides mitjanes, es proposa el càlcul de dues cotes, una d'inferior i una altra de superior, que delimiten un interval de localització de l'esmentat nivell màxim. La tesi està estructurada en els següents capítols. En el capítol 1 es fa una introducció al tema investigat en aquesta tesi i es presenten els treballs existents que el tracten. En el capítol 2 s'introdueixen els models d'estimació de l'energia de transició emprats més habitualment a nivell lògic, que és el nivell de disseny considerat en aquesta tesi. Aquests models assumeixen que l'únic mecanisme de consum és la commutació de les capacitats paràsites del circuit. En els capítols 3 i 4 es tracta l'estimació de l'energia de transició màxima. Aquesta estimació es realitza a partir del càlcul de dues cotes properes, una superior i una altre inferior, a aquesta energia màxima. En el capítol 5 es presenta l'anàlisi del comportament de l'activitat ponderada front als models de retard estàtics. Finalment, en el capítol 6 s'aborden les conclusions generals de la tesis i el treball futur.<br>El consumo energético de los circuitos integrados es un factor cuyo impacto en el diseño electrónico ha crecido significativamente en la década actual. Según el informe de la Semiconductor Industry Association del año 1997, se prevé que este impacto será aún mayor en la próxima década. En la bibliografía existen diversos trabajos donde se relaciona un consumo energético elevado con la degradación de las prestaciones y la fiabilidad del chip. Por esta razón, el consumo energético ha sido incorporado como otro parámetro a tener en cuenta en el diseño de los circuitos integrados. Se conoce como energía de transición la energía consumida por un circuito combinacional CMOS cuando se produce un cambio en las entradas del mismo. Una energía de transición excesivamente elevada puede afectar a la fiabilidad del chip a través de los hot spots, de la electromigración. Otros efectos como el ground bouncing y la signal integrity degradation pueden repercutir en las prestaciones del circuito. La minimización de las degradaciones mencionadas anteriormente requiere de la caracterización de la energía de transición máxima durante la fase de diseño. A este efecto, se propone en esta tesis dos metodologías que permiten la estimación de la energía de transición máxima en circuitos combinacionales CMOS. Dado que la estimación del nivel máximo exacto es inviable en circuitos a partir de tamaños medios, se propone el cálculo de dos cotas, una de inferior y otra de superior, que delimiten un intervalo de localización de dicho nivel máximo. La tesis está estructurada en los siguientes capítulos. En el capítulo 1 se presenta una introducción al tema investigado en esta tesis y se resumen los trabajos existentes más importantes. En el capítulo 2 se introducen los modelos de estimación de la energía de transición más comúnmente utilizados a nivel lógico, que es el nivel de diseño considerado en esta tesis. Estos modelos asumen que el único mecanismo de consumo es la conmutación de las capacidades parásitas del circuito. En los capítulos 3 y 4 se aborda la estimación de la energía de transición máxima. Esta estimación se realiza a partir del cálculo de dos cotas cercanas, una superior y una inferior, a esta energía máxima. En el capítulo 5 se presenta el análisis del comportamiento de la actividad ponderada frente a los modelos de retardo estáticos. Finalmente, en el capítulo 6 se presentan las conclusiones generales de la tesis y el trabajo futuro.<br>The importance of the energy consumption on the design of electronic circuits has increased significantly during the last decade. According to the report of the Semiconductor Industry Association, of 1997, the impact in the next decade will be even greater. In the bibliography several works exist relating to the high energy consumption with the degradation of the reliability and performance of the xip. For this reason, the energy consumption has been included as another parameter to take into account during the design of integrated circuits. It is known as transition energy, the energy consumed by a CMOS combinational circuit when its inputs change their value. Excessively high transition energy may affect the reliability of the chip through the generation of hot spots and electromigration. Other effects such as ground bouncing and signal integrity degradation may reduce the performance of the circuit. In order to minimize the previously detected bad effects it is useful to characterize the maximum transition energy, during the design phase. To this objective, this thesis presents two methodologies that allow for the estimation of the maximum transition energy in CMOS combinational circuits. Given that the estimation of the maximum level is only attainable for medium size circuits, it is proposed the calculation of bounds (higher and lower) delimiting the interval where the maximum level is located. The thesis is divided into the following chapters. In chapter 1 an introduction to the topic and a review of the previous works related to this research domain is given. In chapter 2 the models most extended for the estimation of the transition energy are presented. These models are mainly used at logic level which is the level assumed in this thesis. They assume that the switching of the parasitic capacitances is the only mechanism producing energy consumption. In chapters 3 and 4 the estimation of the maximum transition energy is considered. This estimation is made from the calculation of an upper and lower bound to this maximum transition energy. In chapter 5 the analysis of the switching activity is made for different static delay models. Finally, in chapter 6 the general conclusions of the thesis and future work are discussed.
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Parthasarathy, Prithwick. "Model for energy consumption of 2D Belt Robot : Master’s thesis work." Thesis, Högskolan Väst, Avdelningen för produktionssystem (PS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-9871.
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A production industry with many robots working 24 hours a day, 7 days a week consumes a lot of energy. Industries aim to reduce the energy consumed per machine so as to support their financial budgets and also to be a more sustainable, energy efficient entity. Energy models can be used to predict the energy consumed by robot(s) for optimising the input parameters which determine robot motion and task execution. This work presents an ener-gy model to predict the energy consumption of 2D belt robots used for press line tending. Based on the components' specifications and the trajectory, an estimation of the energy consumption is computed. As part of this work, the proposed energy model is formulated, implemented in MATLAB and experimentally validated. The energy model is further used to investigate the effect of tool weight on energy consumption which includes predicting potential energy reductions achieved by reducing the weight of the gripper tools. Further, investigation of potential energy savings which can be achieved when mechanical brakes are used when the robot is idle is also presented. This illustrates the purpose and usefulness of the proposed energy model.
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Tamaro, Courtney Alex. "Vehicle powertrain model to predict energy consumption for ecorouting purposes." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71635.
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The automotive industry is facing some of the most difficult design challenges in industry history. Developing innovative methods to reduce fossil fuel dependence is imperative for maintaining compliance with government regulations and consumer demand. In addition to powertrain design, route selection contributes to vehicle environmental impact.
The objective of this thesis is to develop a methodology for evaluating the energy consumption of each route option for a specific vehicle. A 'backwards' energy tracking method determines tractive demand at the wheels from route requirements and vehicle characteristics. Next, this method tracks energy quantities at each powertrain component. Each component model is scalable such that different vehicle powertrains may be approximated. Using an 'ecorouting' process, the most ideal route is selected by weighting relative total energy consumption and travel time.
Only limited powertrain characteristics are publicly available. As the future goal of this project is to apply the model to many vehicle powertrain types, the powertrain model must be reasonably accurate with minimal vehicle powertrain characteristics. Future work expands this model to constantly re-evaluate energy consumption with real-time traffic and terrain information.
While ecorouting has been applied to conventional vehicles in many publications, electrified vehicles are less studied. Hybrid vehicles are particularly complicated to model due to additional components, systems, and operation modes. This methodology has been validated to represent conventional, battery electric, and parallel hybrid electric vehicles. A sensitivity study demonstrates that the model is capable of differentiating powertrains with different parameters and routes with different characteristics.<br>Master of Science
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Jansén, Ekberg Ida. "Energy Consumption Model of Cutting Fluid in a Machining System." Thesis, KTH, Industriell produktion, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228059.
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Idag uppmärksammas energiförbrukningen allt mer och mer, både inom privata och industriella sektorer. Emellertid ägnas inom industrin mycket lite uppmärksamhet åt skärvätskesystem. Alltsom oftast är studier gällande vätskesystem mer intresserade av den mängd vätska som används, inte mängden energi.I denna avhandling var syftet att skapa en interaktiv modell som gör det enkelt för användaren att kartlägga energikonsumtionen i sina skärvätskesystem och spåra trender av komponenter av hög energiförbrukning. Modellfunktionerna är huvudsakligen baserade på resultaten av företagsbesök och online litteraturforskning. Modellen har skapats i Microsoft Excel. När modellen byggts användes den för att analysera de observerade systemen. Modellen beräknade energiförbrukningarna och identifierade även pumpar som de högst energikrävande komponenterna för de flesta systemen.Emedan modellen är ganska enkel som den är, beror detta på brist av befintlig information och oförmågan att få information gällande systemdesign och layout på ett en rimligtvis enkelt sätt. Framtida expansioner av modellen diskuteras, och är främst i fråga om utbyte av programvara.<br>In the current environment, energy consumption is gaining more and more attention, both within private and industrial settings. However, within the industrial world, very little attention is being paid to cutting fluid systems. More often than not, fluid system studies are more concerned with the amount of fluid used, not the amount of energy.In this thesis, the purpose was to create an interactive model that makes it easy for the user to map down the energy consumption of their cutting fluid systems and track down trends of components of high energy consumption. The model features are mainly based on the results of company visits and online literature research. The model itself is created in Microsoft Excel.Once the model was built, it was utilized to analyze the observed systems. The model calculated the energy consumptions, and also identified pumps as the major energy consuming components for the majority of the systems.While the model is fairly simple as it stands, this is due to lack of existing information and the inability to gain data rearing system design and layout in a reasonably simplistic form. Future expansion of the model is discussed, and are mainly in regard of software data exchange.
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Mardikar, Yogesh Mukesh. "Establishing baseline electrical energy consumption in wood processing sawmills a model based on energy analysis and diagnostics /." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5412.
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Thesis (Ph. D.)--West Virginia University, 2007.<br>Title from document title page. Document formatted into pages; contains xvi, 222 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 203-207).
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Bazarcheh, Shabestari Negin. "Energy Consumption, CO2 Emissions and Economic Growth : Sweden's case." Thesis, Södertörns högskola, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-35502.
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The main purpose of this study is to examine the causal relations between energy use, CO2 emissions and economic growth for Sweden. Vector Error Correction model with annual data from 1970 to 2016 has been used in order to determine potential causality between the variables. The empirical findings indicate that in the long-run, causality relationship between energy consumption, CO2 emissions and economic growth cannot be rejected and it is bidirectional. This means that energy is a determining factor for economic growth in Sweden and that applying policies in order to reduce the CO2 emissions has slowed down economic growth in Sweden. This finding is consistent with the Feedback Hypothesis. But in the short-run no causality was found between energy and economic growth. According to Granger causality test results, bidirectional causality between CO2 emissions and energy consumption cannot be rejected in the short-run. Variables’ trends show that in the period under study, energy consumption and economic growth have moved in the same direction; meaning that higher energy consumption has led to higher economic growth. At the same time, lower CO2 emissions have been accompanied by higher economic growth. There is also short-run causality running from capital to economic growth according to VECM results. It can be suggested to the policy makers that in order to maintain economic growth and reduce environmental degradation, energy consumption should be shifted gradually from nonrenewable sources to renewable ones so to avoid decrease in economic growth and ensure lower levels of CO2 emissions in the long-run.
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Tasnim, Sumaya. "Renewable Energy Consumption and Foreign Direct Investment : Bangladesh's Case." Thesis, Södertörns högskola, Nationalekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-43739.
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FDI investment is a vital factor for the developing countries economic growth. Apart from working as a catalyst of increasing total output level, FDI is a source of clean energy, technology transfer and energy efficiency. There have been very limited studies on the impact of FDI on renewable energy consumption in the context of Bangladesh. In fact, to my best knowledge there hasn’t been any studies on Bangladesh regarding this relationship with recent data available. Therefore, the aim of this paper is to reveal the relationship between FDI and renewable energy consumption in Bangladesh with annual Data spanning from 1980 to 2016. Johansen’s cointegration test showed that variables are cointegrated in the long run. Through Vector Error Correction Model (VECM), the paper shows there is short run and long run causality between FDI and Renewable Energy Consumption and the causality is negative. Granger causality test reveals that the direction of causality is running from FDI to Renewable Energy Consumption. Policies regarding attracting more sectoral FDI should be considered to improve investment scenario in Renewable energy sector.
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Lee, Sang Hoon. "Management of building energy consumption and energy supply network on campus scale." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43580.
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Building portfolio management on campus and metropolitan scale involves decisions about energy retrofits, energy resource pooling, and investments in shared energy systems, such as district cooling, community PV and wind power, CHP systems, geothermal systems etc. There are currently no tools that help a portfolio/campus manager make these decisions by rapid comparison of variants. The research has developed an energy supply network management tool at the campus scale. The underlying network energy performance (NEP) model uses (1) an existing energy performance toolkit to quantify the energy performance of building energy consumers on hourly basis, and (2) added modules to calculate hourly average energy generation from a wide variety of energy supply systems.
The NEP model supports macro decisions at the generation side (decisions about adding or retrofitting campus wide systems) and consumption side (planning of new building design and retrofit measures). It allows testing different supply topologies by inspecting which consumer nodes should connect to which local suppliers and to which global suppliers, i.e. the electricity and gas utility grids. A prototype software implementation allows a portfolio or campus manager to define the demand and supply nodes on campus scale and manipulate the connections between them through a graphical interface. The NEP model maintains the network topology which is represented by a directed graph with the supply and demand nodes as vertices and their connections as arcs. Every change in the graph automatically triggers an update of the energy generation and consumption pattern, the results of which are shown on campus wide energy performance dashboards.
The dissertation shows how the NEP model supports decision making with respect to large-scale building energy system design with a case study of the Georgia Tech campus evaluating the following three assertions: 1. The normative calculations at the individual building scale are accurate enough to support the network energy performance analysis 2. The NEP model supports the study of the tradeoffs between local building retrofits and campus wide energy interventions in renewable systems, under different circumstances 3. The NEP approach is a viable basis for routine campus asset management policies.
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CESARI, Silvia. "Forecasting and modeling energy consumption of hospital buildings. Relation between energy consumption, architectural features as morphology, layout and medical functions." Doctoral thesis, Università degli studi di Ferrara, 2020. http://hdl.handle.net/11392/2487961.
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Whilst accounting for about 7% of the total built area within the EU non-residential sector, hospitals present the highest energy consumption per unit floor area. They contribute 10% of the total energy use and are estimated to be responsible for roughly 5% of the EU carbon dioxide emissions.
High hospital energy consumption is mostly due to extremely high demand for space heating and cooling throughout the year, caused by the need of high air change rates and the strict requirements for microclimatic control. In addition, the fast-moving advances of the diagnostic techniques require the continuous remodelling of the spaces in existing facilities. This process, besides architectural-distributive aspects, deeply impacts hospital energy consumption, underlining the need of forecasting these new dynamics in relation to the different medical functions.
However, a review of the studies available in literature uncovers that energy analyses are still rarely based on departmental level, thus hospital energy consumption by space type remains largely unexplained. Furthermore, very few works are primarily based on measurement data as well as examine large samples of whole existing structures. This critical knowledge gap, along with a lack of studies exploring hospital energy performance by carefully considering the huge domain of the affecting factors, hinder the construction of more robust and reliable energy benchmarking.
Against this background, the aim of the work consists in the development of a simplified data-driven numerical model able to identify and quantify the dominant end uses in energy consumption costs in relation to the type of the spaces.
Six hospitals located in Northern Italy were taken as case studies and data regarding their medical functions, morphological features of the buildings and levels of insulation, air change rates, operating hours, etc. were analysed in order to build the numerical model, which was then validated with measured energy consumption data of the six facilities. The main aspect of the work is represented by the methodological framework, which has been based on the identification of the energy needs and consumption from micro (single space) to macro scale (macro-area). This method of analysis allows to have energy consumption per unit conditioned floor area and volume by type of medical functions.
Thus, the model developed enables to assess annual thermal energy consumption of hospital buildings – including the energy consumed for summer reheating, DHW and steam used for humidification and sterilisation – in relation to the different type of spaces and to forecast changes in energy consumption related to the refurbishment or modification of these spaces.
The objective of the work, omitting complex dynamic modeling, is also to develop a method as simple as possible, which allows fast obtaining of fairly reliable results, being primarily intended for engineers, architects, technical staff responsible for the maintenance of healthcare facilities and energy managers.
The simplified numerical model does not only allow to assess energy consumption, define effective energy saving measures, cut costs and invest saved resources to improve healthcare. By reducing hospital energy consumption, it represents a robust and reliable instrument to cut healthcare-related carbon emissions.<br>Pur rappresentando circa il 7% della superficie degli edifici appartenenti al settore non residenziale in Europa, gli ospedali sono caratterizzati dal più alto consumo di energia. Essi contribuiscono per il 10% al consumo energetico totale e sono responsabili di circa il 5% delle emissioni di biossido di carbonio a livello europeo.
L'elevato consumo di energia negli edifici ospedalieri è dovuto principalmente ai fabbisogni per riscaldamento e raffrescamento, che sono estremamente alti durante tutto l'anno a causa della necessità di garantire elevati tassi di ventilazione e rispettare i requisiti relativi al controllo microclimatico degli ambienti. Inoltre, i rapidi progressi delle tecniche diagnostiche richiedono la continua riorganizzazione degli spazi nelle strutture sanitarie. Tale processo, oltre a coinvolgere aspetti architettonici e distributivi, incide profondamente sui consumi energetici, sottolineando la necessità di prevedere queste nuove dinamiche in relazione alle diverse funzioni mediche.
Tuttavia, l’esame degli studi disponibili in letteratura rivela che le analisi energetiche sono raramente svolte a livello di reparti ospedalieri. Inoltre, pochissime ricerche sono basate principalmente su dati di misurazione, così come pochissimi lavori esaminano campioni significativi di tipologie di spazi in strutture esistenti. Tale lacuna nell’ambito dell’analisi e previsione dei consumi, insieme alla mancanza di studi che esaminano le prestazioni energetiche degli ospedali considerando attentamente l'enorme dominio dei fattori che su di esse incidono, ostacola lo sviluppo di benchmarking energetici solidi e affidabili.
In questo contesto, l'obiettivo del lavoro è lo sviluppo di un modello numerico semplificato, costruito a partire da dati reali, in grado di identificare e quantificare le voci di costo dell’energia negli edifici ospedalieri in relazione alle tipologie di spazi.
Sei ospedali situati nel nord Italia sono stati selezionati come casi di studio e ne sono stati analizzati i dati relativi ai diversi reparti, le caratteristiche morfologiche degli edifici e i livelli di isolamento, i tassi di ventilazione, le ore di funzionamento degli impianti, ecc. per la costruzione del modello numerico, il quale è stato validato con i dati reali dei consumi energetici degli ospedali considerati.
L'aspetto principale del lavoro è rappresentato dall’approccio metodologico, il quale è basato sull'identificazione dei fabbisogni energetici e dei consumi da micro (singolo spazio) a macro-scala (macro-area). Questo metodo di analisi consente di ottenere i consumi energetici per unità di superficie e per unità di volume in funzione della destinazione d'uso degli spazi.
Pertanto, il modello consente di valutare il consumo annuale di energia termica degli edifici ospedalieri – compresi i consumi per il post-riscaldamento estivo, l'acqua calda sanitaria e la produzione di vapore per l’umidificazione e la sterilizzazione – in relazione ai diversi spazi, e di prevedere le variazioni dei consumi legate alla ristrutturazione o modifica degli spazi stessi.
Scopo del lavoro, omettendo complesse simulazioni energetiche dinamiche, è anche quello di sviluppare un metodo estremamente semplice che consenta di ottenere rapidamente risultati affidabili, essendo principalmente destinato a ingegneri, architetti, al personale tecnico responsabile della gestione delle strutture sanitarie e agli energy managers.
Tale modello non permette solo di valutare i consumi, definire interventi di risparmio energetico efficaci, diminuire i costi relativi e investire le risorse risparmiate per migliorare l'assistenza sanitaria. Riducendo il consumo di energia degli ospedali, il modello sviluppato rappresenta uno strumento robusto per ridurre le emissioni di carbonio che derivano dalle strutture sanitarie.
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Alcántar-Sánchez, Karla. "Housing energy consumption, social behaviour and C02 emissions : a holistic model." Thesis, University of East Anglia, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501749.
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Energy use in houses causes the indirect emission of Carbon Dioxide (CO2) and there is an increasing need to develop effective models to predict such emissions to establish benchmarks against which reductions can be targeted. The Prometheus model described in this thesis is such a tool which was developed under a holistic approach integrating the physical conditions of the house, climatic variations and also social variables all of which affect consumption. The model is sufficiently robust that with minimal modification in the extension of underlying databases it could be used in other areas within the UK.
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Peclinovský, Zdeněk. "Ekonometrická analýza spotřeby energie." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-11819.
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This thesis deals with a real application of econometric methods to the analysis of electric energy consumption in a significant Czech brewery. The main objective is to construct a model predicting the electric energy consumption in the production process in the next week based on various data measured in the last 2 years. Results will be used in the costs management of the company.
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Wright, Andrew John. "The development and use of a model for investigating the thermal behaviour of industrial buildings." Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284684.
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Newsham, Guy R. "Investigating the role of thermal comfort in the assessment of building energy performance using a spatial model." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292780.
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