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Journal articles on the topic 'Energy consumption models'

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Published: 4 June 2025

Last updated: 14 July 2025

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ZHANG, Fa, Antonio Fernandez Anta, Lin WANG, Chen-Ying HOU, and Zhi-Yong LIU. "Network Energy Consumption Models and Energy Efficient Algorithms." Chinese Journal of Computers 35, no. 3 (2012): 603–15. http://dx.doi.org/10.3724/sp.j.1016.2012.00603.

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Akbar, Bilal, Khuram Pervez Amber, Anila Kousar, Muhammad Waqar Aslam, Muhammad Anser Bashir, and Muhammad Sajid Khan. "Data-driven predictive models for daily electricity consumption of academic buildings." AIMS Energy 8, no. 5 (2020): 783–801. http://dx.doi.org/10.3934/energy.2020.5.783.

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Degefa, Mehari Weldemariam. "Ethiopian energy consumption forecast." Mehran University Research Journal of Engineering and Technology 41, no. 4 (2022): 42. http://dx.doi.org/10.22581/muet1982.2204.04.

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This Energy consumption forecast is vital and has a great economic impact. Mathematical models developed for energy forecast can also serve as inputs for further studies. This study is intended to develop an energy consumption forecast using the grey prediction model GM (1,1), based on the actual energy consumption data from the year 2008 to 2017. The models are developed for the total, solid biomass, oil products, and electrical energy consumption; and the accuracy for each model is ratified. These developed forecasting models were used to anticipate six-year Ethiopian consumption of major energy types. The outcomes of models for all four energy consumption types show an upward trend; simulating and forecasting are found suited with the grey system model with development coefficient values less than 0.3 for all selected energy forms.

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Abu, Rahaman, John Amakor, Rasaq Kazeem, et al. "Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria." AIMS Energy 12, no. 1 (2024): 256–70. http://dx.doi.org/10.3934/energy.2024012.

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<abstract> Climate change is having a significant impact on weather variables like temperature, humidity, precipitation, solar radiation, daylight duration, wind speed, etc. These weather variables are key indicators that affect electricity demand and consumption. Hence, understanding the significance of weather elements on energy needs and consumption is important to be able to adapt, strategize, and predict the effect of the changing climate on the required energy of an organization. This study aims to investigate the relationship between changing weather elements and electricity consumption, employing Multivariate Linear Regression (MLR), Support Vector Regressions (SVR), and Artificial Neural Network (ANN) models to predict the effect of weather changes on energy consumption. The following approaches were engaged for this study: Creating a catalog of weather elements and parameters of energy need or its consumption; analyzing and correlating electrical power consumption to weather factors; and developing prediction models—MLR, SVR, and ANN to predict the significance of the change in the variables of weather on the electrical energy consumption. Among the weather variables considered, temperature emerged as the most influential factor affecting electricity consumption, displaying the highest correlation. The monthly total pattern for electricity use for the case study area followed a similar pattern as the mean apparent temperature. Of the three models (MLR, SVR, and ANN) developed in this study, the ANN model yielded the best predictive performance, with Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 2.733%, 1.292%, and 4.66%, respectively. Notably, the ANN model outperformed the other models (MLR and SVR) by more than 20% across the predictive performance metrics employed. </abstract>

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Tang, Lei, Jia Ye Li, Hai Tao Wang, and Lei Chen. "Study on Carbondioxide Emission Reduction and Energy Consumption Structure Adjustment Optimization Models." Advanced Materials Research 524-527 (May 2012): 2433–36. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.2433.

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In modern society, “energy saving and emission reduction” has become a hot issue in scientific research nowadays. In this paper, the emission reduction of CO2 and adjustment scheme on energy consumption structure of the major industries are studied. We conclude that carbondioxide emission of every industry should be reduced. Among them, mining industry has to reduce up to 30.79%. From the adjustment scheme, it is clear that consumptions of electricity power should be controlled because of its low economic efficiency. And the energy consumption structure of transportation industry has higher energy consumption efficiency than other industries. What’s more, the energy consumption structure of water and electricity supply industry needs further adjustments.

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Lairgi, Lamya, Rachid Lagtayi, Yassir Lairgi, et al. "Optimization of tertiary building passive parameters by forecasting energy consumption based on artificial intelligence models and using ANOVA variance analysis method." AIMS Energy 11, no. 5 (2023): 795–809. http://dx.doi.org/10.3934/energy.2023039.

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<abstract> Energy consumption in the tertial sector is largely attributed to cooling/heating energy consumption. Thus, forecasting the building's energy consumption has become a key factor in long-term decision-making, reducing the huge energy demand and future planning. This manuscript outlines to use of the variance analysis method (ANOVA) to study the building's passive parameters' effect, such as the orientation, insulation, and its thickness plus the glazing on energy savings through the forecasting of the heating/cooling energy consumption by applying the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and the Long Short-Term Memory (LSTM) models. The presented methodology compares the predicted consumed energy of a baseline building with another efficient building which includes all the passive parameters selected by the ANOVA approach. The results show that the improvement of passive parameters leads to a reduction of heating energy consumption by 1,739,640 kWh from 2021 to 2029, which is equivalent to a monthly energy consumption of 181.2 kWh for an administrative building with an area of 415 m2. While the cooling energy consumption is diminished by 893,246 kWh from 2021 to 2029, which leads to save a monthly value of 93.05 kWh. Consequently, the passive parameters optimization efficiently reduces the consumed energy and minimizes its costs. This positively impacts our environment due to the reduction of gas emissions, air and soil pollution. </abstract>

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SanMiguel, Juan Carlos, and Andrea Cavallaro. "Energy Consumption Models for Smart Camera Networks." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 12 (2017): 2661–74. http://dx.doi.org/10.1109/tcsvt.2016.2593598.

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Tang, Ning H., N. Nirmalakhandan, and R. E. Speece. "Weir Aeration: Models and Unit Energy Consumption." Journal of Environmental Engineering 121, no. 2 (1995): 196–99. http://dx.doi.org/10.1061/(asce)0733-9372(1995)121:2(196).

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Radhika, Mittal, Singh Ria, and Pooniwala Harsh. "Power Grid Energy Consumption." International Journal of Innovative Science and Research Technology 7, no. 11 (2022): 780–91. https://doi.org/10.5281/zenodo.7395131.

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With the increase in technological advancement, the need for an energy conservation system is increasing. It is necessary to build an effective and predictive system now that existing energy consumption models emphasize on and forecast accuracy. Consumption of energy has increased many folds and now has become a concerning issue. The utilization of energy has increased with human development and growth. The main reasons for these problems are uncontrolled power usage, including excessive consumption, lack of optimal design, and energy wastage. The purpose of this work is to predict the future trend in power usage for any system that monitors and requires this information in real-time. The best model to accomplish this goal was evaluated using recurrent neural networks (RNN) and long short-term memory (LSTM). Results from experiments demonstrate great accuracy and require fewer computer resources during model training than competing models.

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Åsøy, Lene, Houxiang Zhang, and Ann R. Nerheim. "Specific Fuel Oil Consumption Models for Simulating Energy Consumption of Wellboats." Modeling, Identification and Control: A Norwegian Research Bulletin 45, no. 1 (2024): 1–14. http://dx.doi.org/10.4173/mic.2024.1.1.

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Mutani, Guglielmina, Valeria Todeschi, and Simone Beltramino. "Energy Consumption Models at Urban Scale to Measure Energy Resilience." Sustainability 12, no. 14 (2020): 5678. http://dx.doi.org/10.3390/su12145678.

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Energy resilience can be reached with a secure, sustainable, competitive, and affordable system. In order to achieve energy resilience in the urban environment, urban-scale energy models play a key role in supporting the promotion and identification of effective energy-efficient and low-carbon policies pertaining to buildings. In this work, a dynamic urban-scale energy model, based on an energy balance, has been designed to take into account the local climate conditions and morphological urban-scale parameters. The aim is to present an engineering methodology, applied to clusters of buildings, using the available urban databases. This methodology has been calibrated and optimized through an iterative procedure on 102 residential buildings in a district of the city of Turin (Italy). The results of this work show how a place-based dynamic energy balance methodology can also be sufficiently accurate at an urban scale with an average seasonal relative error of 14%. In particular, to achieve this accuracy, the model has been optimized by correcting the typological and geometrical characteristics of the buildings and the typologies of ventilation and heating system; in addition, the indoor temperatures of the buildings—that were initially estimated as constant—have been correlated to the climatic variables. The proposed model can be applied to other cities utilizing the existing databases or, being an engineering model, can be used to assess the impact of climate change or other scenarios.

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Borovsky, Andrey, and Andrey Yumenchuk. "Stochastic Models of Electricity Consumption." System Analysis & Mathematical Modeling 6, no. 1 (2024): 31–46. http://dx.doi.org/10.17150/2713-1734.2024.6(1).31-46.

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The article considers a stochastic model of electricity consumption based on the convolution theory. It is assumed that the processes of switching on and off energy consumption on a city scale depend on a large number of independent random factors and have the form of a normal probability distribution. The functions of switching on and off the load are presented, graphs are plotted. The comparison of the intelligent power supply system with the existing power supply system in the Russian Federation is carried out. The reasons for the slow introduction of smart grids in the Russian Federation are revealed. At the moment, smart grids are not very popular in the Russian Federation, while in the countries of the European Union, the USA and China, projects related to the development, production of components and the widespread introduction of smart grids are actively receiving state financial support. Thus, the European Union allocates more than a billion dollars annually for projects in the field of electric power and smart grids. These funds are allocated according to the European Green Deal strategy. At the same time, the State Grid Corporation of China has introduced the concept of the "Global Energy Internet", which, in accordance with the government's expectation, will stimulate the smart grid market in the country. As in other countries of the world, the US electric power industry is also facing problems related to rising utility prices, fluctuations in peak load and the need to reduce carbon dioxide emissions. The consequence of this was the adoption at the end of 2021 of an "infrastructure" law providing for large-scale investments in green energy projects.

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Ünalan, Hakan, and Emrah Gökaltun. "Alternative window wall ratio of glasses with different solar heat gain coefficient and solar transmittance and their effect on total energy consumption in alternative directions." Journal of Design for Resilience in Architecture and Planning 4, no. 1 (2023): 122–35. http://dx.doi.org/10.47818/drarch.2023.v4i1087.

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Energy simulation model of the building of Eskişehir Technical University Industrial Engineering Department Academic and Administrative Staff rooms were created in this study carried in the scope of energy efficiency and performance of buildings. In the aforementioned energy simulation mode, in line with the International Measurement, Verification and Energy Needs Standards and Protocol (IPMVP) “energy consumption verification”; heating energy, indoor-outdoor environment and climate data were defined, energy consumption verification was carried out and a realistic model was achieved. Using the realistic model achieved, alternative directions were applied to alternative window wall ratios thereby calculating “reference energy consumptions” in “reference building models”. Energy consumptions, calculated by applying alternative glass types to reference models, were then compared with reference energy consumptions

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Ren, Yong, Zhen Ying Mu, Hong Tao Zheng, and Shi Chen. "Energy Consumption Analysis of Ship Energy System." Advanced Materials Research 962-965 (June 2014): 1836–39. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.1836.

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Energy consumption analysis models of ship energy system were established. The performance indexes, such as energy loss ratio, waste heat recovery rate and waste heat recovery perfect degree were defined. A 70000 - ton crude oil carrier was taken as an example for energy consumption analysis. The results show that the waste heat recovery rate of exhaust smoke was 15.69%, and the waste heat recovery perfect degree was 52.76%.

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Buturache, Adrian-Nicolae, and Stelian Stancu. "Building Energy Consumption Prediction Using Neural-Based Models." International Journal of Energy Economics and Policy 12, no. 2 (2022): 30–38. http://dx.doi.org/10.32479/ijeep.12739.

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In the recent years digital transformation became one of the most used approaches in building energy consumption optimization. Increased interest in improving energy sustainability and comfort inside buildings has created an opportunity for digital transformation to build predictive tools for energy consumption. By retrofitting or implementing new construction technologies nowadays the quantity and quality of the operational data collected has reached unprecedented levels. This data must be consumed by implementing powerful predictive tools that will provide the needed level of certainty. Adopting Six Sigma's Define, Measure, Analyze, Improve, Control (DMAIC) cycle as predictive analytics framework will make this paper accessible for both professionals working in energy industry and researchers that are developing models, creating the premises for reducing the gap between research and real-world business, guiding the use of data. Moreover, the selected strategy for preprocessing and hyperparameter selection is presented, the final selected models showing scalability and flexibility. At the end the architectures, performance and training time are discussed and then coupled with the thought process providing a way to weigh up the options. Building energy consumption prediction, it is a relevant and actual topic. Firstly, on European level, meeting the targets set by the new European Green Deal for buildings sector is relying heavily on digitization and therefore on predictive analytics. Secondly, on Romania level, the liberalization of the Energy market created an unpreceded energy price increase. The negative social impact might be diminished not only by the price reduction, but also by understanding how the energy is consumed.

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Oh, Seungmin, Yeonggwang Kim, and Muhammad Firoz Mridha. "Optimizing Energy Consumption Prediction Models using Genetic Algorithms." Journal of Contents Computing 3, no. 1 (2021): 307–16. http://dx.doi.org/10.9728/jcc.2021.06.3.1.307.

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Yang, Shichun, Cheng Deng, Tieqiao Tang, and Yongsheng Qian. "Electric vehicle’s energy consumption of car-following models." Nonlinear Dynamics 71, no. 1-2 (2012): 323–29. http://dx.doi.org/10.1007/s11071-012-0663-0.

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Chanson, Hubert. "Discussion: Weir Aeration: Models and Unit Energy Consumption." Journal of Environmental Engineering 122, no. 4 (1996): 332–33. http://dx.doi.org/10.1061/(asce)0733-9372(1996)122:4(332).

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Krunic, Momcilo V. "Power and Energy Consumption Models for Embedded Applications." Elektronika ir Elektrotechnika 28, no. 5 (2022): 45–54. http://dx.doi.org/10.5755/j02.eie.31345.

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This paper describes a study on the power and energy consumption estimation models that have been defined to facilitate the development of ultra-low power embedded applications. During the study, various measurements have been carried out on the instruction and application level to challenge the models against empirical data. The study has been performed on the multicore heterogeneous hardware platform developed for ultra-low power Digital Signal Processors (DSP) applications. The final goal was to develop a tool that can provide insight into power dissipation during the execution of embedded applications, so that one can refactor the source code in an energy-efficient manner, or ideally to develop an energy-aware C compiler. The side effect of the research presents interesting insight into how the custom hardware architecture influences power dissipation. The selected platform has been chosen simply because it represents R&D state of the art ultra-low power hardware used in hearing aids. The presented solution has been developed and tested in an Eclipse environment using Java programming language.

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Ramos, Paulo Vitor B., Saulo Moraes Villela, Walquiria N. Silva, and Bruno H. Dias. "Residential energy consumption forecasting using deep learning models." Applied Energy 350 (November 2023): 121705. http://dx.doi.org/10.1016/j.apenergy.2023.121705.

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Hua, Lingyun, Jian Tang, and Guoming Zhu. "A Survey of Vehicle System and Energy Models." Actuators 14, no. 1 (2025): 10. https://doi.org/10.3390/act14010010.

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Vehicle system models can be roughly divided into two categories, dynamic and steady-state (or quasi-steady-state) models, and can be applied to evaluate vehicle transient performance such as vehicle longitudinal and lateral dynamics, as well as energy economies like fuel or electricity consumption. This paper reviews various energy consumption models for automotive systems, focusing on component- and vehicle-level models. As the foundation to calculate the energy consumption, powertrain component models of three main vehicle types (internal combustion engine (ICE) vehicles, electric vehicles (EVs), and hybrid vehicles) are reviewed with their key components, including internal combustion engines, electric motors, and batteries. Three types of vehicle energy consumption models are explored according to their interpretability: white-box, black-box, and grey-box models. Optimizing vehicle energy usage based upon a vehicle energy consumption model is reviewed from the aspects of eco-driving and eco-routing problems at the end of the paper. Eco-driving research primarily selects models focusing on transient performance; whereas eco-routing focuses on steady-state or quasi-steady-state conditions to balance the needs of model accuracy and calculation efficiency for real-time applications. This review aims to guide model selection and inspire future applications of energy consumption models for advancing sustainable automotive technologies.

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Ibude, Favour, Abayomi Otebolaku, Jude E. Ameh, and Augustine Ikpehai. "Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks." Journal of Low Power Electronics and Applications 14, no. 4 (2024): 54. http://dx.doi.org/10.3390/jlpea14040054.

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Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

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Pavlicko, Michal, Mária Vojteková, and Oľga Blažeková. "Forecasting of Electrical Energy Consumption in Slovakia." Mathematics 10, no. 4 (2022): 577. http://dx.doi.org/10.3390/math10040577.

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Prediction of electricity energy consumption plays a crucial role in the electric power industry. Accurate forecasting is essential for electricity supply policies. A characteristic feature of electrical energy is the need to ensure a constant balance between consumption and electricity production, whereas electricity cannot be stored in significant quantities, nor is it easy to transport. Electricity consumption generally has a stochastic behavior that makes it hard to predict. The main goal of this study is to propose the forecasting models to predict the maximum hourly electricity consumption per day that is more accurate than the official load prediction of the Slovak Distribution Company. Different models are proposed and compared. The first model group is based on the transverse set of Grey models and Nonlinear Grey Bernoulli models and the second approach is based on a multi-layer feed-forward back-propagation network. Moreover, a new potential hybrid model combining these different approaches is used to forecast the maximum hourly electricity consumption per day. Various performance metrics are adopted to evaluate the performance and effectiveness of models. All the proposed models achieved more accurate predictions than the official load prediction, while the hybrid model offered the best results according to performance metrics and supported the legitimacy of this research.

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Katipamula, S., and D. E. Claridge. "Use of Simplified System Models to Measure Retrofit Energy Savings." Journal of Solar Energy Engineering 115, no. 2 (1993): 57–68. http://dx.doi.org/10.1115/1.2930033.

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The retrofit of dual-duct constant volume systems (DDCV) with energy-efficient variable air volume systems (VAV) has become common in recent years. In general, the energy savings from such retrofits are estimated by developing a temperature-dependent regression model using whole building preretrofit energy consumption data. Model predictions are then compared with measured post retrofit consumption, to determine the savings. In cases where the preretrofit energy consumption is not available such a method cannot be implemented. This paper describes a method that can be used to calculate savings in such cases. The method is based on use of simplified calibrated system models. A VAV model was developed based on the ASHRAE TC 4.7 Simplified Energy Analysis Procedure (SEAP) (Knebel, 1983) and calibrated with the postretrofit energy consumption of a large engineering center in Central Texas. The loads from the calibrated VAV model were then used with the DDCV model to estimate the preretrofit energy use, also based on TC 4.7 SEAP, and apparent savings were determined as the difference between the DDCV predicted consumption and measured energy consumption for the postretrofit VAV system. The simulated hourly cooling energy consumption from the VAV model was within ±1GJ (±20 percent) of the measured consumption. The simulated daily consumption (the sum of 24 hours of consumption) compared better with the measured daily consumption (within ±7 percent). The apparent saving from the retrofit of the DDCV system with VAV was about 684 GJ in cooling energy and 324 GJ in heating energy for a three-week period June–July 1991.

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Chen, Yanyan, Siyang Li, and Yanan Li. "A Review on Quantitative Energy Consumption Models from Road Transportation." Energies 17, no. 1 (2023): 2. http://dx.doi.org/10.3390/en17010002.

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With the objectives of achieving “peak carbon” and “carbon neutrality”, accurately quantifying the carbon emissions of road transportation becomes crucial. It is challenging to accurately describe the energy consumption of vehicles at both temporal and spatial scales from a macro perspective. Therefore, focusing on the quantitative model of vehicle micro energy consumption and road meso energy consumption, this paper reviewed and summarized the energy consumption model of road traffic in terms of data collection, quantification accuracy, and scope of application. Based on this analysis, this paper identifies the challenges of the current road traffic energy consumption model. Finally, we look forward to future research directions for studying quantitative models of energy consumption from road transportation.

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Rozas, Wolfram, Rafael Pastor-Vargas, Angel Miguel García-Vico, and José Carpio. "Consumption–Production Profile Categorization in Energy Communities." Energies 16, no. 19 (2023): 6996. http://dx.doi.org/10.3390/en16196996.

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Energy Transition is changing the renewable energy participation in new distributed generation systems like the Local Energy Markets. Due to its inherent intermittent and variable nature, forecasting production and consumption load profiles will be more challenging and demand more complex predictive models. This paper analyzes the production, consumption load profile, and storage headroom% of the Cornwall Local Energy Market, using advanced statistical time series methods to optimize the opportunity market the storage units provide. These models also help the Energy Community storage reserves to meet contract conditions with the Distribution Network Operator. With this more accurate and detailed knowledge, all sites from this Local Energy Market will benefit more from their installation by optimizing their energy consumption, production, and storage. This better accuracy will make the Local Energy Market more fluid and safer, creating a flexible system that will guarantee the technical quality of the product for the whole community. The training of several SARIMAX, Exponential Smoothing, and Temporal Causal models improved the fitness of consumption, production, and headroom% time series. These models properly decomposed the time series in trend, seasonality, and stochastic dynamic components that help us to understand how the Local Energy Market consumes, produces, and stores energy. The model design used all power flows and battery energy storage system state-of-charge site characteristics at daily and hourly granularity levels. All model building follows an analytical methodology detailed step by step. A benchmark between these sequence models and the incumbent forecasting models utilized by the Energy Community shows a better performance measured with model error reduction. The best models present mean squared error reduction between 88.89% and 99.93%, while the mean absolute error reduction goes from 65.73% to 97.08%. These predictive models built at different prediction scales will help the Energy Communities better contribute to the Network Management and optimize their energy and power management performance. In conclusion, the expected outcome of these implementations is a cost-optimal management of the Local Energy Market and its contribution to the needed new Flexibility Electricity System Scheme, extending the adoption of renewable energies.

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Mouakher, Amira, Wissem Inoubli, Chahinez Ounoughi, and Andrea Ko. "Expect: EXplainable Prediction Model for Energy ConsumpTion." Mathematics 10, no. 2 (2022): 248. http://dx.doi.org/10.3390/math10020248.

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With the steady growth of energy demands and resource depletion in today’s world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called Expect, to forecast energy consumption from time series effectively. Our results demonstrate our proposal’s robustness and accuracy when compared to the baseline methods.

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Nie, Peng, Michele Roccotelli, Maria Pia Fanti, and Zhiwu Li. "Fuzzy rule-based models for home energy consumption prediction." Energy Reports 8 (November 2022): 9279–89. http://dx.doi.org/10.1016/j.egyr.2022.07.054.

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Pao, H. T. "Forecasting energy consumption in Taiwan using hybrid nonlinear models." Energy 34, no. 10 (2009): 1438–46. http://dx.doi.org/10.1016/j.energy.2009.04.026.

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Kara, S., and W. Li. "Unit process energy consumption models for material removal processes." CIRP Annals 60, no. 1 (2011): 37–40. http://dx.doi.org/10.1016/j.cirp.2011.03.018.

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McDonald, J. David, Richard J. Kerekes, and Joe R. Zhao. "Wet Pressing Models to Reduce Energy Consumption in Papermaking." Paper and Biomaterials 4, no. 1 (2019): 1–6. http://dx.doi.org/10.26599/pbm.2019.9260001.

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Dingil, Ali Enes, Joerg Schweizer, Federico Rupi, and Zaneta Stasiskiene. "Updated Models of Passenger Transport Related Energy Consumption of Urban Areas." Sustainability 11, no. 15 (2019): 4060. http://dx.doi.org/10.3390/su11154060.

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Introduction: As the global warming threat has become more concrete in recent years, there is a need to update transport energy consumptions of cities and to understand how they relate to population density and transport infrastructure. Transportation is one of the major sources of global warming and this update is an important warning for urban planners and policy makers to take action in a more consistent way. Analysis: This paper estimates and analyzes the passenger transport energy per person per year with a large and diverse sample set based on comparable, directly observable open-source data of 57 cities, distributed over 33 countries. The freight transport energy consumption, which accounts for a large portion of urban transport energy, is not considered. The main focus of the analysis is to establish a quantitative relation between population density, transport infrastructure and transport energy consumption. Results: In a first step, significant linear relations have been found between road length per inhabitant, the road infrastructure accessibility (RIA) and private car mode share as well as between RIA and public transport mode share. Results show further relation between travel distance, population density and RIA. In a second step, a simplified model has been developed that explains the non-linear relation between the population density and RIA. Finally, based on this relation and the above findings, a hyperbolic function between population density and transport energy has been calibrated, which explains the rapid increase of transport energy consumption of cities with low population density. Conclusions: The result of the this study has clearly identified the high private car mode share as main cause for the high transport energy usage of such cities, while the longer average commute distance in low-population density cities has a more modest influence on their transport energy consumption.

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Velchev, Stefan, Ivan Kolev, Krasimir Ivanov, and Simeon Gechevski. "Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning." Journal of Cleaner Production 80 (October 2014): 139–49. http://dx.doi.org/10.1016/j.jclepro.2014.05.099.

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Muhire, Francis. "The Drivers of green energy consumption in East African Community." Journal of Financial and Management Sciences; Vol. 1 No. 1 (2025): Special Issue: Sustainable Development 1, no. 1 (2025): 6–38. https://doi.org/10.70970/0pxaza37.

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The The study examines the effects of environmental policy quality, GDP per capita, quality of policy and institutional frameworks, regulatory effectiveness, population growth, electricity access, and research and development investments on green energy consumption in the East African Community (EAC). The study employed the FMOLS and CCR models for analysis. Data was sourced from the World Development Indicators (WDI) and International Energy Agency (IEA) for the EAC from 2000 to 2022. The study found that regulatory quality, quality of environmental policies, and access to electricity has a positive and significant long-run effect on green energy consumption in the EAC. However, the study also found that GDP per capita and the Quality of Institutions and Policies do not have a long-run effect on green energy consumption in the EAC. Given the global attention to Green Energy Consumption as a solution to climate change and to meet energy needs, this study discloses less studied drivers of Green Energy Consumption (as a proxy of Green Energy Transition), especially “Quality of environmental policies” in the EAC. Furthermore, most existing studies focus on renewable energy consumption, which includes solid biomass such as charcoal and firewood, while this study covers green energy consumption.

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Wan, Hang, Michael David, and William Derigent. "Energy-efficient chain-based data gathering applied to communicating concrete." International Journal of Distributed Sensor Networks 16, no. 8 (2020): 155014772093902. http://dx.doi.org/10.1177/1550147720939028.

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Wireless Sensor Networks are very convenient to monitor structures or even materials, as in McBIM project (Materials communicating with the Building Information Modeling). This project aims to develop the concept of “communicating concretes,” which are concrete elements embedding wireless sensor networks, for applications dedicated to Structure Health Monitoring in the construction industry. Due to applicative constraints, the topology of the wireless sensor network follows a chain-based structure. Node batteries cannot be replaced or easily recharged, it is crucial to evaluate the energy consumed by each node during the monitoring process. This area has been extensively studied leading to different energy models to evaluate energy consumption for chain-based structures. However, no simple, practical, and analytical network energy models have yet been proposed. Energy evaluation models of periodic data collection for chain-based structures are proposed. These models are compared and evaluated with an Arduino XBee–based platform. Experimental results show the mean prediction error of our models is 5%. Realizing aggregation at nodes significantly reduces energy consumption and avoids hot-spot problem with homogeneous consumptions along the chain. Models give an approximate lifetime of the wireless sensor network and communicating concretes services. They can also be used online by nodes for a self-assessment of their energy consumptions.

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Shivam Shukla, Greesham Anand, and Shubham Agarwal. "A Survey of Energy Consumption Models for Electric Vehicles: From Simulation to Real-World Applications." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 2, no. 2 (2025): 15–28. https://doi.org/10.63503/j.ijaimd.2025.96.

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Electric vehicles (EVs) are essential to low-carbon mobility. For optimal performance, infrastructure development, and energy efficiency, EV adoption requires accurate and reliable energy consumption models. This study discusses the latest EV energy consumption modelling advances, pinpointing four main aspects: vehicle components, dynamics, traffic circumstances, and environmental factors. EV energy consumption models are classified by scale (microscopic vs. macroscopic) and methodology (data-driven vs. rule-based). Microscopic models analyse driving behaviours to estimate short-term energy usage, while macroscopic models estimate trip-level energy consumption for large-scale planning. The paper also notes a shift towards data-driven models, which use machine learning and massive datasets for accuracy. Rule-based models use physical concepts and empirical formulas. Many research gaps remain despite advances. Energy models for electric buses, lorries, and industrial vehicles are needed. Vehicle-to-grid (V2G) integration models need improvement to allow bidirectional energy exchange. Finally, multiscale energy models, which combine microscopic and macroscopic techniques, may improve EV energy estimation accuracy and application. This study highlights emerging trends and future research goals, emphasising scalable, intelligent, and flexible energy consumption models for EV uptake and sustainable mobility.

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Bartouli, Monia, Imen Hagui, Amina Msolli, Abdelhamid Helali, and Fredj Hassen. "Smart Grid Load Forecasting Models Using Recurrent Neural Network and Long Short-Term Memory." Jordan Journal of Electrical Engineering 11, no. 1 (2025): 1. http://dx.doi.org/10.5455/jjee.204-1703066445.

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This paper presents two methods for managing electrical energy consumption and demand, with the objective of developing reliable and accurate forecasting models for smart electrical network energy consumption and optimization. The first method utilizes a recurrent neural network (RNN), while the second employs long short-term memory (LSTM) techniques. This approach builds upon previous studies that have explored the use of machine learning models for energy forecasting, but often with limited performance or the inability to capture long-term dependencies in the data. The study utilizes the Global Energy Forecast 2012 database - for the period from 2004 to 2008, with a focus on electricity consumption - to validate the performance of the proposed models. The R-squared (R2) score is used as the primary evaluation metric, with the LSTM model achieving a remarkable 90% R2 score, outperforming the RNN model's 80% R2 score. This is a significant improvement over previous studies, which have typically reported R2 scores in the range of 70-80% for energy forecasting models. Furthermore, the LSTM model demonstrates superior error rate performance, with a Mean Squared Error (MSE) of 4.345%, compared to the RNN model's 16.644% MSE. This highlights the ability of LSTM models to capture long-term dependencies in the data, which is crucial for accurate energy consumption forecasting, a limitation often observed in traditional RNN-based approaches. The findings of this study highlight the superior performance of the LSTM-based approach in accurately predicting energy consumption in smart grids, a crucial aspect for optimizing energy management and distribution. This contribution is particularly significant, as it showcases the advantages of LSTM models over traditional RNN techniques in the context of energy forecasting, providing valuable insights for researchers and practitioners in the field of smart grid optimization, where accurate forecasting is essential for efficient energy management and distribution.

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Oskarbski, Jacek, and Konrad Biszko. "Estimation of Vehicle Energy Consumption at Intersections Using Microscopic Traffic Models." Energies 16, no. 1 (2022): 233. http://dx.doi.org/10.3390/en16010233.

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This paper addresses issues related to modeling energy consumption and emissions using microscopic traffic simulations. This paper develops a method in which a traffic model is used to calculate the energy needed to travel through selected types of intersections. This paper focuses on energy consumption and derived values of calculated energy, which can be, for example, carbon dioxide emissions. The authors present a review of the scientific literature on the study of factors affecting energy consumption and emissions and methods to estimate them in traffic. The authors implemented an energy consumption model into a microsimulation software module to estimate results as a function of varying traffic volumes at selected types of intersections and for selected traffic organization scenarios. The results of the study show the lowest energy consumption and the lowest emissions when road solutions are selected that contribute to reducing vehicle travel times on the urban street network at higher average vehicle speeds. In addition, the positive impact of the share of electric vehicles in the traffic flow on the reduction of energy consumption and emissivity was estimated.

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Sabo, Kristian, Rudolf Scitovski, Ivan Vazler, and Marijana Zekić-Sušac. "Mathematical models of natural gas consumption." Energy Conversion and Management 52, no. 3 (2011): 1721–27. http://dx.doi.org/10.1016/j.enconman.2010.10.037.

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Alnori, Abdulaziz, Karim Djemame, and Yousef Alsenani. "Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing." Applied Sciences 14, no. 6 (2024): 2385. http://dx.doi.org/10.3390/app14062385.

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The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

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Rypdal, Kristoffer. "Empirical growth models for the renewable energy sector." Advances in Geosciences 45 (July 25, 2018): 35–44. http://dx.doi.org/10.5194/adgeo-45-35-2018.

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Abstract. Three simple, empirical models for growth of power consumption in the renewable energy sector are compared. These are the exponential, logistic, and power-law models. The exponential model describes growth at a fixed relative growth rate, the logistic model saturates at a fixed limit, while the power-law model describes slowing, but unlimited, growth. The model parameters are determined by regression to historical global data for solar and wind power consumption, and model projections are compared to scenarios based on macroeconomic modelling that meet the 2∘ target. It is demonstrated that rational rejection of an exponential growth model in favour of a logistic growth model cannot be made from existing data for the historical evolution of global renewable power consumption y(t). It is also shown that the logistic model yields saturation of growth at unrealistic low levels. The power-law growth model is found to give very good fits to the data through the last decade, and the projections align very well with the scenarios. Power-law growth is equivalent to the simple law that the relative growth rate y′/y decays inversely proportional to time. It is shown that this is a natural model for growth that slows down due to various constraints, yet not experiencing the effect of a strict upper limit defined by physical boundaries. If the actual consumption follows the power-law curve in the years to come the exponential-growth null hypothesis can be correctly rejected around 2020.

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Wang, Qi, Dinghua Zhang, Bing Chen, Ying Zhang, and Baohai Wu. "Energy Consumption Model for Drilling Processes Based on Cutting Force." Applied Sciences 9, no. 22 (2019): 4801. http://dx.doi.org/10.3390/app9224801.

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Accurate energy consumption modelling is critical for the improvement of energy efficiency in machining. Existing energy models of machining processes mainly focus on turning or milling, and there are few energy models for drilling. However, since drilling is often applied to roughing and semi-finishing, and the cutting parameters are large, the energy consumption is huge, and it is urgent to study the consumption of energy during the drilling process. In this paper, an energy consumption model for drilling processes was proposed. Idle power, cutting power, and auxiliary power were included in the proposed energy consumption model, using the cutting force to obtain the cutting power during drilling. Further, the relationship between cutting power and auxiliary power was analyzed. Cutting experiments were then carried out which confirmed the correctness of the proposed model. In addition, compared with several existing energy consumption models, the proposed model had better accuracy and applicability. It is expected that the proposed energy consumption model will have applications for the minimization of energy consumption and improvement of energy efficiency but not limited to only drilling energy consumption prediction.

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Oliveira, Hugo S., and Helder P. Oliveira. "Transformers for Energy Forecast." Sensors 23, no. 15 (2023): 6840. http://dx.doi.org/10.3390/s23156840.

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Forecasting energy consumption models allow for improvements in building performance and reduce energy consumption. Energy efficiency has become a pressing concern in recent years due to the increasing energy demand and concerns over climate change. This paper addresses the energy consumption forecast as a crucial ingredient in the technology to optimize building system operations and identifies energy efficiency upgrades. The work proposes a modified multi-head transformer model focused on multi-variable time series through a learnable weighting feature attention matrix to combine all input variables and forecast building energy consumption properly. The proposed multivariate transformer-based model is compared with two other recurrent neural network models, showing a robust performance while exhibiting a lower mean absolute percentage error. Overall, this paper highlights the superior performance of the modified transformer-based model for the energy consumption forecast in a multivariate step, allowing it to be incorporated in future forecasting tasks, allowing for the tracing of future energy consumption scenarios according to the current building usage, playing a significant role in creating a more sustainable and energy-efficient building usage.

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Chudy-Laskowska, Katarzyna, and Tomasz Pisula. "Forecasting Household Energy Consumption in European Union Countries: An Econometric Modelling Approach." Energies 16, no. 14 (2023): 5561. http://dx.doi.org/10.3390/en16145561.

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The article raises issues regarding the consumption of energy from both fossil and renewable sources in households. The research was carried out on the basis of data obtained from the Eurostat database, which covered the period from 1995 to 2021 and concerned the European Union countries. Increasing energy consumption and, thus, increasing household expenses affect their standard of living. The purpose of the analysis was to construct two econometric models for electricity consumption. The first model referred to the consumption of energy from fossil sources and the second from renewable sources. A forecast of energy consumption in households was also constructed on the basis of estimated models. Econometric modelling methods (multiple regression) and time-series forecasting methods (linear regression method, exponential smoothing models) were applied for the study. Research shows that the main factor that models energy consumption in households, both from fossil and renewable sources, is the final consumption expenditure of households (Euro per capita). The set of indicators for the models varies depending on the type of energy source. The forecast shows that the share of energy consumption obtained from fossil sources will decrease systematically, while the share of energy consumption from renewable sources will continue to increase systematically.

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Bejarano, Gissella, David DeFazio, and Arti Ramesh. "Deep Latent Generative Models for Energy Disaggregation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 850–57. http://dx.doi.org/10.1609/aaai.v33i01.3301850.

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Thoroughly understanding how energy consumption is disaggregated into individual appliances can help reduce household expenses, integrate renewable sources of energy, and lead to efficient use of energy. In this work, we propose a deep latent generative model based on variational recurrent neural networks (VRNNs) for energy disaggregation. Our model jointly disaggregates the aggregated energy signal into individual appliance signals, achieving superior performance when compared to the state-of-the-art models for energy disaggregation, yielding a 29% and 41% performance improvement on two energy datasets, respectively, without explicitly encoding temporal/contextual information or heuristics. Our model also achieves better prediction performance on lowpower appliances, paving the way for a more nuanced disaggregation model. The structured output prediction in our model helps in accurately discerning which appliance(s) contribute to the aggregated power consumption, thus providing a more useful and meaningful disaggregation model.

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Mohamed, El Fissaoui, Benkirane Said, Beni-Hssane Abderrahim, and Saadi Mostafa. "Scalability Aware Energy Consumption and Dissipation Models for Wireless Sensor Networks." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (2017): 424–31. https://doi.org/10.11591/ijece.v7i1.pp424-431.

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Most of Wireless Sensor Networks researches focus on reducing the amount of energy consumed by nodes and network to increase the network lifetime. Thus, several papers have been presented and published to optimize energy consumption in each area of WSNs, such as routing, localization, coverage, security, etc. To test and evaluate their propositions, authors apply an energy dissipation model; this model must be more realistic and suitable to give good results. In this paper we present a general preview on different sources of energy consumption in wireless sensor networks, and provide a comparative study between two energy models used in WSNs that offer an effective and an adequate tool for researchers.

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Sánchez-Mompó, Adrián, Ioannis Mavromatis, Peizheng Li, Konstantinos Katsaros, and Aftab Khan. "Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations." Information 16, no. 4 (2025): 281. https://doi.org/10.3390/info16040281.

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This study presents an empirical investigation into the energy consumption of discriminative and generative AI models within real-world MLOps pipelines. For discriminative models, we examine various architectures and hyperparameters during training and inference and identify energy-efficient practices. For generative AI, large language models (LLMs) are assessed, with a focus primarily on energy consumption across different model sizes and varying service requests. Our study employs software-based power measurements, ensuring ease of replication across diverse configurations, models, and datasets. We analyse multiple models and hardware setups to uncover correlations among various metrics, identifying key contributors to energy consumption. The results indicate that, for discriminative models, optimising architectures, hyperparameters, and hardware can significantly reduce energy consumption without sacrificing performance. For LLMs, energy efficiency depends on balancing model size, reasoning complexity, and request-handling capacity, as larger models do not necessarily consume more energy when utilisation remains low. This analysis provides practical guidelines for designing green and sustainable ML operations, emphasising energy consumption and carbon-footprint reductions while maintaining performance. This paper can serve as a benchmark for accurately estimating total energy use across different types of AI models.

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Singh, Pranavi, Nilima Zade, Prashant Priyadarshi, and Aditya Gupte. "The Application of Machine Learning and Deep Learning Techniques for Global Energy Utilization Projection for Ecologically Responsible Energy Management." International Journal of Advances in Soft Computing and its Applications 17, no. 1 (2025): 48–63. https://doi.org/10.15849/ijasca.250330.04.

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Accurately estimating future energy consumption is critical as the world seeks alternatives to fossil fuels amidst rising energy demands. The research employs various prediction models for global energy prediction with GDP analysis in energy consumption context. These models include Regression models that are Linear, Polynomial, Bayesian, Tree, Extreme Gradient Boosting, K Nearest Neighbour, Stacked Model, Random Forest (RF), also Long Short-Term Memory (LSTM) and Convolution Neural Networks (CNN) methods. Models are employed to enhance global energy consumption modelling, analysing their adaptability to varying weather and social conditions. A comparative investigation shows that RF performs better than other Regression models. LSTM models perform better than RF in predicting the primary energy consumption per capita and GDP growth, with the lowest MSE value of 0.002 with comparatively higher time and processing complexity. However, RF outperforms in predicting renewable energy share, access to clean cooking fuel, CO2 emission and GDP per capita analysis. The study's novelty lies in its comprehensive evaluation of machine learning and deep learning methods across multiple geographic and temporal energy consumption patterns, emphasizing the superiority of advanced techniques in accurately modelling global energy usage.

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Jung, Dae Kyo, Dong Hwan Lee, Joo Ho Shin, Byung Hun Song, and Seung Hee Park. "Optimization of Energy Consumption Using BIM-Based Building Energy Performance Analysis." Applied Mechanics and Materials 281 (January 2013): 649–52. http://dx.doi.org/10.4028/www.scientific.net/amm.281.649.

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Recently, the interest in increasing energy efficiency of building energy management system (BEMS) has become a high-priority and thus the related studies also increased. In particular, since the energy consumption in terms of heating and cooling system takes a large portion of the energy consumed in buildings, it is strongly required to enhance the energy efficiency through intelligent operation and/or management of HVAC (Heating, Ventilation and Air Conditioning) system. To tackle this issue, this study deals with the BIM (Building Information Modeling)-based energy performance analysis implemented in Energyplus. The BIM model constructed at Revit is updated at Design Builder, adding HVAC models and converted compatibly with the Energyplus environment. And then, the HVAC models are modified throughout the comparison between the energy consumption patterns and the real-time monitoring in-field data. In order to maximize the building energy performance, a genetic algorithm (GA)-based optimization technique is applied to the modified HVAC models. Throughout the proposed building energy simulation, finally, the best optimized HVAC control schedule for the target building can be obtained in the form of “supply air temperature schedule”.

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Natarajan, Yuvaraj, Sri Preethaa K. R., Gitanjali Wadhwa, et al. "Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction." Sustainability 16, no. 5 (2024): 1925. http://dx.doi.org/10.3390/su16051925.

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Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks’ features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study’s innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future.

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