Academic literature on the topic 'Electricity load profile data'

End-use specific residential electricity load profiles are of interest for energy system modelling that requires future load curves or demand-side management. We present a model that is applicable across countries to predict consumption on a regional and national scale, using openly available data. The model uses neural networks (NNs) to correlate measured consumption from one country (United Kingdom) with weather data and daily profiles of a mix of human activity and device specific power profiles. We then use region-specific weather data and time-use surveys as input for the trained NNs to predict unscaled electric load profiles. The total power profile consists of the end-use household load profiles scaled with real consumption. We compare the model’s results with measured and independently simulated profiles of various European countries. The NNs achieve a mean absolute error compared with the average load of 6.5 to 33% for the test set. For Germany, the standard deviation between the simulation, the standard load profile H0, and measurements from the University of Applied Sciences Berlin is 26.5%. Our approach reduces the amount of input data required compared with existing models for modelling region-specific electricity load profiles considering end-uses and seasonality based on weather parameters. Hourly load profiles for 29 European countries based on four historical weather years are distributed under an open license.

It is important to understand residential energy use as it is a large energy consumption sector and the potential for change is of great importance for global energy sustainability. A large energy-saving potential and emission reduction potential can be achieved, among others, by understanding energy consumption patterns in more detail. However, existing studies show that it requires many input parameters or disaggregated individual end-uses input data to generate the load profiles. Therefore, we have developed a simplified approach, called weighted proportion (Wepro) model, to synthesise the residential electricity load profile by proportionally matching the city’s main characteristics: Age group, labour force and gender structure with the representative households profiles provided in the load profile generator. The findings indicate that the synthetic load profiles can represent the local electricity consumption characteristics in the case city of Amsterdam based on time variation analyses. The approach is in particular advantageous to tackle the drawbacks of the existing studies and the standard load model used by the utilities. Furthermore, the model is found to be more efficient in the computational process of the residential sector’s load profiles, given the number of households in the city that is represented in the local profile.

Strategies for meeting Sustainable Development Goal 7 of providing access to electricity for all recognize the important role that off-grid solutions will need to play. Mini-grids will from part of this response, yet little data exists on household demand from these customers. Predicting demand accurately is a crucial part of planning financially viable mini-grid systems, so it is important to understand demand as fully as possible. This paper draws on metered data from two solar PV diesel hybrid mini-grid sites in Tanzania. It presents an analysis of load profiles from the different sites and categorizes households by demand characteristics. The paper then combines load profile data with household demographic and electrical asset ownership data to explore factors behind distinct load profile patterns of use. It concludes that load profiles are determined by a complex mix of appliance ownership, occupancy, and socio-economic status.

This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for the proposed PF based load prediction. The management efficiency of microgrid is significantly improved by using the PF method.

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance.

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
A presente dissertação tem por objetivo o estudo quantitativo da previsão da demanda de carga elétrica semanal para a região sudeste e em particular, para os Estados do Rio de Janeiro e São Paulo. Foram estudadas para tanto as séries reais dos últimos 7(sete) anos, ou seja, de janeiro de 1991 a novembro de 1997 das concessionárias LIGHT, CERJ, CESP, CPFL e ELETROPAULO. Para o estudo de previsão foi utilizado o conceito in sample, ou seja, parte real dos dados foram separados e mais tarde comparados com os valores previstos experimentalmente para aquela mesma época dos dados reais separados. Desta forma, permitiu-se averiguar qual seria a precisão da previsão, verificando-se os erros entre os valores experimentais e reais. Para os cálculos das previsões, também foi utilizado o conceito de bayesiano de combinação de previsões (outperformance) das duas técnicas a saber: redes neurais artificiais (software Neunet) e o modelo clássico Box & Jenkins (software Autobox). Para se obter o valor combinado das previsões, foi utilizado software matlab que se comportou de maneira adequada para o estudo em questão. Além disso vale acrescentar que o software Neunet foi utilizado, pois possui em seu ambiente a técnica de eliminação de sinapses enquadra-se dentro do conceito de redes neurais multicamadas com retropropagação dos erros.
The goal of this dissertation is to present a quantitative study in time series of weekly electrical charge demand at the southeast region, particulary at Rio de Janeiro and São Paulo. In this work will be analysed the last 7 years, from january 1991 to november of 1997. The next time series were study: LIGHT, CERJ, CESP, CPFL and ELETROPAULO. Aimming to test the model against real data the concept of sample data was utilized in this dissertation. Another concept used in this work was outperformance. Outperformance is a Bayesian concept that involves the combination of two or more techniques in order to enchance the forecasting results. Artificial neural network and Box and Jenkins method are combined in this work. It is also interesting to notice that weight elimination, which is a new ANN technique, proved to be faster then classical back- propagation and yielded better results.

En 2010, ERDF (le distributeur d'électricité Français) a entamé la mise en place du projet " Linky " dont l'objectif est d'installer 35 millions de compteurs intelligents en France. Ces compteurs permettront de collecter les données de consommation en " temps réel ", avec lesquelles des modèles de charge plus précis pourront être envisagés. Dans ce contexte, cette thèse définit deux objectifs: la définition de modèles prédictifs de charge pour la conduite et la conception de modèles d'estimation de charge pour la planification. En ce qui concerne la conduite, nous avons développés deux modèles. Le premier exploite le formalisme mathématique des séries chronologiques ; le second est basé sur le réseau de neurones. Les deux modèles cherchent à prévoir la charge des jours " J+1 " et " J+2 " à partir des informations collectées jusqu'au jour " J ". Quand à la planification, un modèle non paramétrique est proposé et comparé avec le modèle actuel " BAGHEERA " d'EDF. Le modèle non paramétrique est un modèle individuel configuré par les relevées compteurs. Trois régresseurs non paramétriques (Nadaraya Watson, Local Linear et Local Linear adapted) sont proposés. Les scénarios de validation montrent que le modèle non paramétrique est plus précis que le modèle " BAGHEERA ". Ces nouveaux modèles ont été conçus et validés sur de vraies données collectées sur le territoire français.

En 2010, ERDF (Electricité Réseau Distribution France) a entamé la mise en place du projet « Linky » dont l'objectif est d'installer 35 millions de compteurs intelligents en France. Ces compteurs permettront de collecter les données de consommation en « temps réel », avec lesquelles des modèles de charge plus précis pourront être envisagés. Dans ce contexte, cette thèse définit deux objectifs: la définition de modèles prédictifs de charge pour la conduite et la conception de modèles d'estimation de charge pour la planification. En ce qui concerne la conduite, nous avons développés deux modèles. Le premier exploite le formalisme mathématique des séries chronologiques ; le second est basé sur un réseau de neurones. Les deux modèles cherchent à prévoir la charge des jours « J+1 » et « J+2 » à partir des informations collectées jusqu'au jour « J ». Le modèle « série chronologique » repose sur les propriétés temporelles des courbes de charge. Ainsi on découpe la courbe de charge en trois parties : la tendance, la périodicité et le résidu. Les premiers deux sont déterministes et indépendamment développés en deux modèles : le modèle de tendance et le modèle de cyclicité. La somme de la prévision de ces deux modèles est la prévision finale. Le résidu quant à lui capture les phénomènes aléatoires que présente la courbe de charge. Le modèle de prédiction ainsi développé s'aide de nombreux outils statistiques (e.g., test de stationnarité, test ANOVA, analyse spectrale, entres autres) pour garantir son bon fonctionnement. Enfin, modèle « série chronologique » prend en compte plusieurs facteurs qui expliquent la variation dans la courbe de consommation tels que la température, les cyclicités, le temps, et le type du jour, etc. En ce qui concerne le modèle à base de réseaux de neurones, nous nous focalisons sur les stratégies de sélection de la structure pour un modèle optimal. Les choix des entrées et du nombre de neurones cachés sont effectués à travers les méthodes dites de «régression orthogonale » et de « leave-one-out-virtuel ». Les résultats montrent que la procédure proposée permet de choisir une structure de réseau de neurones qui garantisse une bonne précision de prédiction. En ce qui concerne la planification, un modèle non paramétrique est proposé et comparé avec le modèle actuel « BAGHEERA » d'EDF. Avec l'ouverture du marché d'électricité, la relation entre les fournisseurs, les clients et les distributeurs devient flexible. Les informations qualitatives d'un client particulier telles que sa puissance souscrite, son code d'activité, ses tarifs etc. sont de moins en moins disponibles. L'évolution du modèle BAGHEERA qui dépend ces informations pour classer les clients dans différentes catégories est devenue indispensable. Le modèle non paramétrique est un modèle individuel centré sur le relevé des compteurs. Trois variables de régression non paramétriques : Nadaraya Watson, Local Linear et Local Linear adapted ont été analysées et comparées. Les scénarios de validation montrent que le modèle non paramétrique est plus précis que le modèle « BAGHEERA ». Ces nouveaux modèles ont été conçus et validés sur de vraies données collectées sur le territoire français
From 2010, ERDF (French DSO) started the “Linky” project. The project aims at installing 35 millions smart meters in France. These smart meters will collect individual client's consumption data in real time and transfer these data to the data center automatically in a certain frequency. These detailed consumption information provided by the smart metering system enables the designs of more accurate load models. On this purpose, two distinctive objectives are defined in this dissertation: the forecasting load models for the operation need and the estimation load models for the planning need. For the operation need, two models are developed, respectively relying on the “time series” and the “neural network” principals. They are both for the objective of predicting the loads in “D+1” and “D+2” days based on the historical information till “D” day. The “time series” model divides the load curve into three components: the trend, the cyclic, and the residual. The first two parts are deterministic, from which two models named the trend model and the cyclic model are made. The sum of the prevision of these two models is the final prediction result. For a better precision, numerous statistical tools are also integrated such that the stationary test, the smoothed periodogram, the ANOVA test and the gliding window estimation, etc. The time series model can extract information from the influence factors such as the time, the temperature, the periodicities and the day type, etc. Being the most popular non linear model and the universal approximator, the neural network load forecasting model is also studied in this dissertation. We focus on the strategy of the structure selection. The work is in collaboration with Prof. Dreyfus (SIGMA lab), a well known expert in the machine learning field. Input selection and model selection are performed by the “orthogonal forward regression” and the “virtual-leave-one-out” algorithms. Results show that the proposed procedure is efficient and guarantees the chosen model a good accuracy on the load forecasting. For the planning, a nonparametric model is designed and compared with the actual model “BAGHEERA” of the French electricity company EDF. With the opening of the electricity market, the relationship among the regulators, suppliers and clients is changing. The qualitative information about a particular client such as his subscribed power, his activity code and his electricity tariffs becomes less and less available. The evolution from the BAGHEERA model to a data-driven model is unavoidable, since the BAGHEERA model depends on these information to attribute every client in the French territory into a pre-defined category. The proposed nonparametric model is individualized and can deal with both temperature sensitive (possessing an electrical heater) and temperature insensitive clients. Three nonparametric regressors are proposed: the Nadaraya Watson, the local linear, and the local linear adapted. The validation studies show that the nonparametric model has a better estimation precision than the BAGHEERA model. These novel models are designed and validated by the real measurements collected in the French distribution network

[EN] The electricity sector is currently undergoing a process of liberalization and separation of roles, which is being implemented under the regulatory auspices of each Member State of the European Union and, therefore, with different speeds, perspectives and objectives that must converge on a common horizon, where Europe will benefit from an interconnected energy market in which producers and consumers can participate in free competition. This process of liberalization and separation of roles involves two consequences or, viewed another way, entails a major consequence from which other immediate consequence, as a necessity, is derived. The main consequence is the increased complexity in the management and supervision of a system, the electrical, increasingly interconnected and participatory, with connection of distributed energy sources, much of them from renewable sources, at different voltage levels and with different generation capacity at any point in the network. From this situation the other consequence is derived, which is the need to communicate information between agents, reliably, safely and quickly, and that this information is analyzed in the most effective way possible, to form part of the processes of decision taking that improve the observability and controllability of a system which is increasing in complexity and number of agents involved. With the evolution of Information and Communication Technologies (ICT), and the investments both in improving existing measurement and communications infrastructure, and taking the measurement and actuation capacity to a greater number of points in medium and low voltage networks, the availability of data that informs of the state of the network is increasingly higher and more complete. All these systems are part of the so-called Smart Grids, or intelligent networks of the future, a future which is not so far. One such source of information comes from the energy consumption of customers, measured on a regular basis (every hour, half hour or quarter-hour) and sent to the Distribution System Operators from the Smart Meters making use of Advanced Metering Infrastructure (AMI). This way, there is an increasingly amount of information on the energy consumption of customers, being stored in Big Data systems. This growing source of information demands specialized techniques which can take benefit from it, extracting a useful and summarized knowledge from it. This thesis deals with the use of this information of energy consumption from Smart Meters, in particular on the application of data mining techniques to obtain temporal patterns that characterize the users of electrical energy, grouping them according to these patterns in a small number of groups or clusters, that allow evaluating how users consume energy, both during the day and during a sequence of days, allowing to assess trends and predict future scenarios. For this, the current techniques are studied and, proving that the current works do not cover this objective, clustering or dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users are developed. These techniques are tested and validated on a database of hourly energy consumption values for a sample of residential customers in Spain during years 2008 and 2009. The results allow to observe both the characterization in consumption patterns of the different types of residential energy consumers, and their evolution over time, and to assess, for example, how the regulatory changes that occurred in Spain in the electricity sector during those years influenced in the temporal patterns of energy consumption.
[ES] El sector eléctrico se halla actualmente sometido a un proceso de liberalización y separación de roles, que está siendo aplicado bajo los auspicios regulatorios de cada Estado Miembro de la Unión Europea y, por tanto, con distintas velocidades, perspectivas y objetivos que deben confluir en un horizonte común, en donde Europa se beneficiará de un mercado energético interconectado, en el cual productores y consumidores podrán participar en libre competencia. Este proceso de liberalización y separación de roles conlleva dos consecuencias o, visto de otra manera, conlleva una consecuencia principal de la cual se deriva, como necesidad, otra consecuencia inmediata. La consecuencia principal es el aumento de la complejidad en la gestión y supervisión de un sistema, el eléctrico, cada vez más interconectado y participativo, con conexión de fuentes distribuidas de energía, muchas de ellas de origen renovable, a distintos niveles de tensión y con distinta capacidad de generación, en cualquier punto de la red. De esta situación se deriva la otra consecuencia, que es la necesidad de comunicar información entre los distintos agentes, de forma fiable, segura y rápida, y que esta información sea analizada de la forma más eficaz posible, para que forme parte de los procesos de toma de decisiones que mejoran la observabilidad y controlabilidad de un sistema cada vez más complejo y con más agentes involucrados. Con el avance de las Tecnologías de Información y Comunicaciones (TIC), y las inversiones tanto en mejora de la infraestructura existente de medida y comunicaciones, como en llevar la obtención de medidas y la capacidad de actuación a un mayor número de puntos en redes de media y baja tensión, la disponibilidad de datos sobre el estado de la red es cada vez mayor y más completa. Todos estos sistemas forman parte de las llamadas Smart Grids, o redes inteligentes del futuro, un futuro ya no tan lejano. Una de estas fuentes de información proviene de los consumos energéticos de los clientes, medidos de forma periódica (cada hora, media hora o cuarto de hora) y enviados hacia las Distribuidoras desde los contadores inteligentes o Smart Meters, mediante infraestructura avanzada de medida o Advanced Metering Infrastructure (AMI). De esta forma, cada vez se tiene una mayor cantidad de información sobre los consumos energéticos de los clientes, almacenada en sistemas de Big Data. Esta cada vez mayor fuente de información demanda técnicas especializadas que sepan aprovecharla, extrayendo un conocimiento útil y resumido de la misma. La presente Tesis doctoral versa sobre el uso de esta información de consumos energéticos de los contadores inteligentes, en concreto sobre la aplicación de técnicas de minería de datos (data mining) para obtener patrones temporales que caractericen a los usuarios de energía eléctrica, agrupándolos según estos mismos patrones en un número reducido de grupos o clusters, que permiten evaluar la forma en que los usuarios consumen la energía, tanto a lo largo del día como durante una secuencia de días, permitiendo evaluar tendencias y predecir escenarios futuros. Para ello se estudian las técnicas actuales y, comprobando que los trabajos actuales no cubren este objetivo, se desarrollan técnicas de clustering o segmentación dinámica aplicadas a curvas de carga de consumo eléctrico diario de clientes domésticos. Estas técnicas se prueban y validan sobre una base de datos de consumos energéticos horarios de una muestra de clientes residenciales en España durante los años 2008 y 2009. Los resultados permiten observar tanto la caracterización en consumos de los distintos tipos de consumidores energéticos residenciales, como su evolución en el tiempo, y permiten evaluar, por ejemplo, cómo influenciaron en los patrones temporales de consumos los cambios regulatorios que se produjeron en España en el sector eléctrico durante esos años.
[CAT] El sector elèctric es troba actualment sotmès a un procés de liberalització i separació de rols, que s'està aplicant davall els auspicis reguladors de cada estat membre de la Unió Europea i, per tant, amb distintes velocitats, perspectives i objectius que han de confluir en un horitzó comú, on Europa es beneficiarà d'un mercat energètic interconnectat, en el qual productors i consumidors podran participar en lliure competència. Aquest procés de liberalització i separació de rols comporta dues conseqüències o, vist d'una altra manera, comporta una conseqüència principal de la qual es deriva, com a necessitat, una altra conseqüència immediata. La conseqüència principal és l'augment de la complexitat en la gestió i supervisió d'un sistema, l'elèctric, cada vegada més interconnectat i participatiu, amb connexió de fonts distribuïdes d'energia, moltes d'aquestes d'origen renovable, a distints nivells de tensió i amb distinta capacitat de generació, en qualsevol punt de la xarxa. D'aquesta situació es deriva l'altra conseqüència, que és la necessitat de comunicar informació entre els distints agents, de forma fiable, segura i ràpida, i que aquesta informació siga analitzada de la manera més eficaç possible, perquè forme part dels processos de presa de decisions que milloren l'observabilitat i controlabilitat d'un sistema cada vegada més complex i amb més agents involucrats. Amb l'avanç de les tecnologies de la informació i les comunicacions (TIC), i les inversions, tant en la millora de la infraestructura existent de mesura i comunicacions, com en el trasllat de l'obtenció de mesures i capacitat d'actuació a un nombre més gran de punts en xarxes de mitjana i baixa tensió, la disponibilitat de dades sobre l'estat de la xarxa és cada vegada major i més completa. Tots aquests sistemes formen part de les denominades Smart Grids o xarxes intel·ligents del futur, un futur ja no tan llunyà. Una d'aquestes fonts d'informació prové dels consums energètics dels clients, mesurats de forma periòdica (cada hora, mitja hora o quart d'hora) i enviats cap a les distribuïdores des dels comptadors intel·ligents o Smart Meters, per mitjà d'infraestructura avançada de mesura o Advanced Metering Infrastructure (AMI). D'aquesta manera, cada vegada es té una major quantitat d'informació sobre els consums energètics dels clients, emmagatzemada en sistemes de Big Data. Aquesta cada vegada major font d'informació demanda tècniques especialitzades que sàpiguen aprofitar-la, extraient-ne un coneixement útil i resumit. La present tesi doctoral versa sobre l'ús d'aquesta informació de consums energètics dels comptadors intel·ligents, en concret sobre l'aplicació de tècniques de mineria de dades (data mining) per a obtenir patrons temporals que caracteritzen els usuaris d'energia elèctrica, agrupant-los segons aquests mateixos patrons en una quantitat reduïda de grups o clusters, que permeten avaluar la forma en què els usuaris consumeixen l'energia, tant al llarg del dia com durant una seqüència de dies, i que permetent avaluar tendències i predir escenaris futurs. Amb aquesta finalitat, s'estudien les tècniques actuals i, en comprovar que els treballs actuals no cobreixen aquest objectiu, es desenvolupen tècniques de clustering o segmentació dinàmica aplicades a corbes de càrrega de consum elèctric diari de clients domèstics. Aquestes tècniques es proven i validen sobre una base de dades de consums energètics horaris d'una mostra de clients residencials a Espanya durant els anys 2008 i 2009. Els resultats permeten observar tant la caracterització en consums dels distints tipus de consumidors energètics residencials, com la seua evolució en el temps, i permeten avaluar, per exemple, com van influenciar en els patrons temporals de consums els canvis reguladors que es van produir a Espanya en el sector elèctric durant aquests anys.
Benítez Sánchez, IJ. (2015). Dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59236
TESIS

Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented.
Libyan government

Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented.

Operational performance of grid-connected microgrid with integrated solar photovoltaic (PV) electricity production and battery energy storage (BES) is investigated.  These distributed energy resources (DERs) have the potential to reduce conventionally produced electrical power and contribute to reduction of greenhouse gas emissions.  This investigation is based upon the DER’s techno-economic specifications and theoretical performance, consumer load data and electrical utility retail and distribution data.  Available literature provides the basis for DER specification and performance.  Actual consumer load profile data is available for residential and commercial consumer sector customers.  The electrical utility data is obtained from Mälarenergi, AB.  The aim is to investigate how to use simulations to specify a grid connected microgrid with DERs (PV production and a BES system) for two consumer sectors considering a range of objectives.  An open-source, MATLAB-based simulation tool called Opti-CE has successfully been utilized.  This package employs a genetic algorithm for multi-objective optimization.  To support attainment of one of the objectives, peak shaving of the consumer load, a battery operational strategy algorithm has been developed for the simulation.  With respect to balancing peak shaving and self-consumption one of the simulations supports specification of a commercial sector application with 117 kWp PV power rating paired with a lithium ion battery with 41.1 kWh capacity.  The simulation of this system predicts the possibility to shave the customer load profile peaks for the month of April by 20%.  The corresponding self-consumption ratio is 88%.  Differences in the relationship between the load profiles and the system performance have been qualitatively noted.  Furthermore, simulation results for lead-acid, lithium-ion and vanadium-redox flow battery systems are compared to reveal that lithium ion delivers the best balance between total annualized cost and peak shaving performance for both residential and commercial applications.

The aim of this study is to investigate, analyse and evaluate the installed off-grid PV system in Mavuno girls’ secondary school that is located in a rural area in northwest Tanzania. The original motivation behind this study was the rapid degradation of the installed battery bank within less than 3 years. The PV system was installed before the actual operation of the school, so the study aimed to answer a very pressing question which is "What is the actual load profiles in the school?". There was a high need to identify the actual school load profiles to enable several concerned social actors to evaluate the system and to decide for future extensions. Therefore, the study aimed to analyse the implementation of electricity in the school by creating actual load profiles, analysing the system performance versus the users’ needs and evaluating the sustainability and utilization of implementation. The study followed a multi-disciplinary approach combining the social and technical aspects of PV systems implementation to seek further understanding of the users’ consumption behaviours. It thus included a 1-month of field work in June 2018 during which participant observations and semi-structured interviews together with load measurements were carried out so as to create load profiles that are considering the patterns and deviations in users’ behaviours. During the field work, 2/3 of the students were in holidays so the taken measurements corresponded to the school at 30 % capacity. That is why the study also included 4 days of inverter data logging after the 1-month field work by the technical head of the school to overcome the limitations in held measurements. The observations showed that the actual installed system was slightly different from the documentation. In addition, the local installation practices are not fully appropriate from the technical point of view, and are affected by local social norms, as will be discussed. Besides, the participant observations and held interviews with relevant social actors showed that the daily behaviours of energy users do not exactly follow the school daily routine. Consequently, the social study was important to create actual effective load profiles. The observations and responses from interviews together with measurements were used to categorize the school loads into 29 different units. Those units can be used for current load prioritizations and for future load extrapolations. The created load profiles also represent a useful addition to load databases used by energy researchers who work on similar rural electrification projects. After the field work, several characteristics were calculated by Microsoft Excel such as apparent power consumptions, active power consumptions, battery bank state of charge, load power factor and PV generated energy. The characteristics were used in calculations evaluating the energy balance in the system. The results of held calculations showed that lighting during dark hours accounted for on around 78 % of the logged daily apparent energy use, as it has a low a low average power factor of 0.28. It also showed that some loads if time-bounded, they will significantly decrease the daily energy consumption. The calculations were also used to run PVSyst simulations to evaluate the system sizing which resulted in the recommendation that either the array size should be doubled, or the apparent energy consumption should be decreased to half. The study included suggestions for possible improvements such as decreasing the reactive consumed energy by either replacing the currently used light bulbs with ones that have higher power factor ( ≥0.8 for example) or by installing a capacitive compensation for power factor correction. In addition, it was recommended to quantify the school loads according to their priority or importance and to regulate observed time-unbounded loads such as "pumping water" and "ironing". Lastly, the study discussed how generated electricity is utilized in the school and what opportunities for women empowerment have become potentially possible with the provision of electricity.

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.
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.

An accurate demand forecasting is essential for planning the electric dispatch in power system, contributing financially to electricity companies and helping in the security and continuity of electricity supply. In addition, it is evident that the distributed energy resource integration in the electric power system has been increasing recently, mostly from the photovoltaic generation, resulting in a gradual change of the load curve profile. Therefore, the 24 hours ahead prediction of the electrical demand of Campina Grande, Brazil, was realized from artificial neural network with a focus on the data preprocessing. Thus, the time series variations, such as hourly, diary and seasonal, were reduced in order to obtain a better demand prediction. Finally, it was compared the results between the forecasting with the preprocessing application and the prediction without the preprocessing stage. Based on the results, the first methodology presented lower mean absolute percentage error with 7.95% against 10.33% of the second one.

Net metering is an incentive that is essential to most solar photovoltaic systems. Recently the burden placed upon local utilities is an issue some regulators have been asked to address. This research uses actual 2013 and 2014 solar production data from nearly 200 sites, wholesale electricity day-ahead pricing data, and utility-wide demand data. This is all analyzed by the hour for two full years for a western Pennsylvania based utility and an eastern Pennsylvania based utility and their wholesale generators. Results show electricity is 15% more valuable when solar PV systems are generating power and feeding the grid during good weather conditions than at night or cloudy days when solar customers get energy back from the grid. Solar energy generation is highly predictable in the day-ahead market, and leads to suppression in market prices for electricity. Thus to reveal the true impact of this market suppression, an increased solar renewable portfolio standard (RPS) fraction of 0.2 to 10% was simulated. This caused a decrease in demand resulting in a corresponding reduction in the price of electricity yielding savings to the utility. The maximum rate of increase and decrease in the utility-wide load did not change significantly until the solar RPS exceeded 5%. Additionally, the demand for electricity was reduced during the highest load hours of the year that corresponded to the most expensive hours of the year. The minimum base-load of the year was decreased substantially for solar RPS of 5% or greater and the base load reaches zero for solar RPS over 10%. From the data of these two years, it is demonstrated that an increased use of solar energy would lead to savings that are larger than the loss in revenue due to having fewer traditional non-solar customers. Thus electricity suppliers and utilities stand to have both higher profits and higher profit margins when customers adopt net-metered solar energy compared to the non-adoption of solar energy.

The variability of wind and solar is perceived as a major obstacle in employing otherwise abundant renewable energy resources. Based on the available geographically dispersed data for the WECC U.S. area (excluding Alaska) and eastern U.S., we analyze to what extent the geographic diversity of these resources can offset their variability. It is common to discern baseload (i.e. constantly employed cheaper power generation, as from nuclear and coal plants) from more expensive dispatchable power sources which help meet variable electric load. The spot electricity price depends on the difference between the load and baseload. With significant amounts of power coming from wind and solar, we use generation with low variable cost (GLVC) to include baseload and wind/solar generation. The GLVC will then become variable as well. The electricity price, however, will be determined by the difference between load and GLVC. While the details of future electricity spot-pricing are harder to predict, the overall trend will remain: a higher hourly difference between load and the low-variable-cost generation increases the electricity price. This difference can serve as an approximate measure of the (hourly) revenue from producing electricity. Currently, the variable load follows fairly well defined daily and weekly load cycles. Significant amount of wind-produced power will inevitably alter the cyclic nature for the variable load time-dependence. Additionally, wind and solar farms generation profiles may be expected to poorly correlate with the variable load. We determine the set of wind and PV sites that best matches the load; we also show that the generation from any wind or PV site from the optimal set is positively correlated with the remaining variable load. The geographic distribution of optimal generation sites, e.g. North vs. South, shows features similar to phase transitions.

Building energy contributes approximately 40% of U.S. greenhouse gas emissions and 75% of emissions in some urban areas. Evaluating modifications to existing building stocks is essential to a proper assessment of GHG reduction policy at various levels. With deeper penetration of intermittent renewable energy resources, supply and demand effects at a high resolution (e.g. hourly) will become more important as variations in grid emissions will become more significant. City-level hourly electricity load data is available; however, effects of building stock changes on usage profiles are not easily analyzed, and on-site fossil fuel usage — the dominant loads in many urban areas — are generally only available annually. Building energy models allow for detailed simulation of building systems, but existing building models must be calibrated to actual energy usage to predict the effects of energy conservation measures. Reference building models developed by the U.S. Department of Energy for the EnergyPlus software tool were used as the basis for a set of calibrated building energy models to perform community-scale energy conservation measures on the dominant building classes in NYC (i.e. residential and office buildings). A statistical analysis of zip code-level annual electricity and fuel usage data was performed to determine electricity, space heating fuel and domestic hot water (DHW) fuel usage intensities (EUIs) for three broad building categories encompassing these building types in New York City. Several parameters were adjusted for each model until simulations produced the EUIs from the statistical analysis: Thermal envelope characteristics, peak electric equipment and lighting loads, DHW flow requirements, cooling equipment coefficient of performance and heating equipment efficiency. Cooling energy demands were adjusted based on the electricity demand vs. temperature behavior during the cooling season. The hourly daily usage schedules of internal electric and lighting loads were then adjusted for all models, targeting the actual hourly electricity demands for NYC. Because hourly changes affect annual EUIs, the calibrations were performed iteratively until the model outputs, weighted by each building type’s total NYC square footage, equaled the annual EUIs for each building type and the hourly electricity demand data. This paper shows that this comprehensive calibration approach can achieve root-mean-square deviation (RMSD) of 7% from the average annual electricity demand for these building types, compared to a 31% RMSD for an approach using annual energy calibration only.

Distributed cogeneration systems typically face variable electrical and heating demands, especially when applied to civil loads (e.g. residential, tertiary and commercial loads), often showing significant variations during the day of the heat to electricity load ratio. In these cases, application of heat storage gives the possibility of significantly improving the energetic and economic performance of the system. This paper discusses the development and testing of an optimization model for the selection of the best operating strategy and component sizing for cogeneration systems with heat storage, based on the code DCOGEN already presented in a previous work. The model employs a multiple-step optimization approach, discussed in detail in the paper, aiming to select the best hourly operating schedule on a daily basis. Optimization allows to (i) minimize or eliminate the waste of useful heat of the cogeneration prime mover (e.g. a microturbine or a reciprocating engine) and (ii) maximize the economic benefit of running the prime mover at high load, selling excess electricity to the grid when convenient. Results show that an optimized system with heat storage can reduce the thermal power produced by the auxiliary boiler and substantially increases the primary energy savings of the cogeneration unit. The model is tested towards a real application, with time-variable loads and several day-types profiles. The discussion compares different size cogeneration systems, based on micro gas turbine and internal combustion engines. Test case specifications also include tariffs, regulatory and climate data, provided that performance of the main components are corrected as a function of load and ambient conditions. Detailed results are presented, in terms of annual energy balances, energy savings and economic analysis.