Academic literature on the topic 'Electric power consumption – econometric models'
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Journal articles on the topic "Electric power consumption – econometric models"
Karpenko, S. M., N. V. Karpenko, and G. Y. Bezginov. "Forecasting of power consumption at mining enterprises using statistical methods." Mining Industry Journal (Gornay Promishlennost), no. 1/2022 (March 15, 2022): 82–88. http://dx.doi.org/10.30686/1609-9192-2022-1-82-88.
Full textDieudonné, Nzoko Tayo, Talla Konchou Franck Armel, Aloyem Kaze Claude Vidal, and Tchinda René. "Prediction of electrical energy consumption in Cameroon through econometric models." Electric Power Systems Research 210 (September 2022): 108102. http://dx.doi.org/10.1016/j.epsr.2022.108102.
Full textShin, Sun-Youn, and Han-Gyun Woo. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms." Energies 15, no. 13 (July 2, 2022): 4880. http://dx.doi.org/10.3390/en15134880.
Full textChudy-Laskowska, Katarzyna, and Tomasz Pisula. "Forecasting Household Energy Consumption in European Union Countries: An Econometric Modelling Approach." Energies 16, no. 14 (July 23, 2023): 5561. http://dx.doi.org/10.3390/en16145561.
Full textAnh, Le Hoang, Gwang Hyun Yu, Dang Thanh Vu, Jin Sul Kim, Jung Il Lee, Jun Churl Yoon, and Jin Young Kim. "Stride-TCN for Energy Consumption Forecasting and Its Optimization." Applied Sciences 12, no. 19 (September 20, 2022): 9422. http://dx.doi.org/10.3390/app12199422.
Full textGajdzik, Bożena, Włodzimierz Sroka, and Jolita Vveinhardt. "Energy Intensity of Steel Manufactured Utilising EAF Technology as a Function of Investments Made: The Case of the Steel Industry in Poland." Energies 14, no. 16 (August 20, 2021): 5152. http://dx.doi.org/10.3390/en14165152.
Full textThakare, Sameer, Neeraj Dhanraj Bokde, and Andrés E. Feijóo-Lorenzo. "Forecasting different dimensions of liquidity in the intraday electricity markets: A review." AIMS Energy 11, no. 5 (2023): 918–59. http://dx.doi.org/10.3934/energy.2023044.
Full textSun, Zhenhua, Lingjun Du, and Houyin Long. "Regional Heterogeneity Analysis of Residential Electricity Consumption in Chinese Cities: Based on Spatial Measurement Models." Energies 16, no. 23 (November 30, 2023): 7859. http://dx.doi.org/10.3390/en16237859.
Full textPeng, Fei, Ye Zhang, Guohua Song, Jianchang Huang, Zhiqiang Zhai, and Lei Yu. "Evaluation of Real-World Fuel Consumption of Hybrid-Electric Passenger Car Based on Speed-Specific Vehicle Power Distributions." Journal of Advanced Transportation 2023 (February 27, 2023): 1–13. http://dx.doi.org/10.1155/2023/9016510.
Full textMyszczyszyn, Janusz, and Błażej Suproń. "Relationship among Economic Growth, Energy Consumption, CO2 Emission, and Urbanization: An Econometric Perspective Analysis." Energies 15, no. 24 (December 19, 2022): 9647. http://dx.doi.org/10.3390/en15249647.
Full textDissertations / Theses on the topic "Electric power consumption – econometric models"
Lee, Cheuk-wing, and 李卓穎. "Transmission expansion planning in a restructured electricity market." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38959410.
Full textYan, Yonghe, and 嚴勇河. "A multi-agent based approach to transmission cost allocation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B3124256X.
Full textNyulu, Thandekile. "Weather neutral models for short-term electricity demand forecasting." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1018751.
Full textEnzinger, Sharn Emma 1973. "The economic impact of greenhouse policy upon the Australian electricity industry : an applied general equilibrium analysis." Monash University, Centre of Policy Studies, 2001. http://arrow.monash.edu.au/hdl/1959.1/8383.
Full textFachrizal, Reza. "Synergy between Residential Electric Vehicle Charging and Photovoltaic Power Generation through Smart Charging Schemes : Models for Self-Consumption and Hosting Capacity Assessments." Licentiate thesis, Uppsala universitet, Byggteknik och byggd miljö, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-419665.
Full textIinuma, Yoshiki. "Scale economies, technological change and capacity factor an economic analysis of thermal power generation in Japan /." Thesis, 1991. http://catalog.hathitrust.org/api/volumes/oclc/27161958.html.
Full textRavele, Thakhani. "Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions." Diss., 2018. http://hdl.handle.net/11602/1165.
Full textDepartment of Statistics
Forecasting of electricity peak demand levels is important for decision makers in Eskom. The overall objective of this study was to develop medium term load forecasting models which will help decision makers in Eskom for planning of the operations of the utility company. The frequency table of hourly daily demands was carried out and the results show that most peak loads occur at hours 19:00 and 20:00, over the period 2009 to 2013. The study used generalised additive models with and without tensor product interactions to forecast electricity demand at 19:00 and 20:00 including daily peak electricity demand. Least absolute shrinkage and selection operator (Lasso) and Lasso via hierarchical interactions were used for variable selection to increase the model interpretability by eliminating irrelevant variables that are not associated with the response variable, this way also over tting is reduced. The parameters of the developed models were estimated using restricted maximum likelihood and penalized regression. The best models were selected based on smallest values of the Akaike information criterion (AIC), Bayesian information criterion (BIC) and Generalized cross validation (GCV) along with the highest Adjusted R2. Forecasts from best models with and without tensor product interactions were evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). Operational forecasting was proposed to forecast the demand at hour 19:00 with unknown predictor variables. Empirical results from this study show that modelling hours individually during the peak period results in more accurate peak forecasts compared to forecasting daily peak electricity demand. The performance of the proposed models for hour 19:00 were compared and the generalized additive model with tensor product interactions was found to be the best tting model.
NRF
Thanyani, Maduvhahafani. "Forecasting hourly electricity demand in South Africa using machine learning models." Diss., 2020. http://hdl.handle.net/11602/1595.
Full textDepartment of Statistics
Short-term load forecasting in South Africa using machine learning and statistical models is discussed in this study. The research is focused on carrying out a comparative analysis in forecasting hourly electricity demand. This study was carried out using South Africa’s aggregated hourly load data from Eskom. The comparison is carried out in this study using support vector regression (SVR), stochastic gradient boosting (SGB), artificial neural networks (NN) with generalized additive model (GAM) as a benchmark model in forecasting hourly electricity demand. In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso). The SGB model yielded the least root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) on testing data. SGB model also yielded the least RMSE, MAE and MAPE on training data. Forecast combination of the models’ forecasts is done using convex combination and quantile regres- sion averaging (QRA). The QRA was found to be the best forecast combination model ibased on the RMSE, MAE and MAPE.
NRF
CHEN, SHANG-YI, and 陳尚毅. "Development of the models and controls of community microgrids with PV and battery energy storage for the assessment of residential-type users’ electric power consumption." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/z2e56v.
Full text國立中正大學
電機工程研究所
105
Domestic energy consumption is mainly divided into industrial power, agricultural power, commercial power and residential power, etc. Economic development, climatic factors and population size are common factors which influence the energy consumption and bring on gradual increase of energy consumption. In the economy, the development of heavy industry is required to consume so much energy that the energy consumption increases as well. People's daily life is also closely related to energy consumption. For the general residential users, the gradual increase in load demand will lead to power outage crisis. Therefore, the above problems can be reduced after the community-based micro-grid which is composed of renewable energy generation, storage and control system incorporated into the system. Furthermore, the community-based micro-grid not only can monitor the load demand and power supply but also it can save customers money and utilize energy more efficiently at the same time. To save the cost of testing on physical system, we could verify the feasibility of the proposed method through the system simulations. This thesis analyzes the cases based on actual community-based micro-grid system with construction of renewable energy sources, storage system and controller models and proposes some controlling strategies. Moreover, real-time simulation techniques are used to resolve limitations of off-line simulations and simulation analysis is implemented in condition of grid mode and island mode and limiting power, etc. In the thesis, load forecasting is also executed to extend the functions of simulation system. With the implementation of system simulations, the results show that it not only brings economic benefit for customers but also validate the efficiency of the proposed methods and controlling strategies.
Books on the topic "Electric power consumption – econometric models"
Losembe, Remy Bolito. Les dépenses en électricité à Kinshasa: Une étude empirique (cas des ménages). Kinshasa, RDC: I.R.E.S., 2004.
Find full textKulindwa, Kassim. Residential electricity in Tanzania: The case of Dar es Salaam. [Dar es Salaam]: University of Dar es Salaam, Economic Research Bureau, 1996.
Find full textM, Bolet Adela, and Georgetown University. Center for Strategic and International Studies., eds. Forecasting U.S. electricity demand: Trends and methodologies. Boulder: Westview Press, 1985.
Find full textDollinger, Manfred. Eine ökonometrische Analyse der Elektrizitätsnachfrage und der Elektrizitätsproduktion in der Bundesrepublik Deutschland. Idstein: Schulz-Kirchner, 1988.
Find full textKokkelenberg, Edward Charles. Oil shocks and the demand for electricity. Ithaca, N.Y: Dept. of Agricultural Economics, New York State College of Agriculture and Life Sciences, Cornell University, 1992.
Find full textDavid, Hawdon, ed. Energy demand: Evidence and expectations. London: Surrey University Press, 1992.
Find full textLin, Bo Q. Electricity demand in the People's Republic of China: Investment requirement and environmental impact. Manila: Asian Development Bank, 2003.
Find full textLin, Bo Q. Electricity demand in the People's Republic of China: Investment requirement and environmental impact. Manila, Philippines: Asian Development Bank, 2003.
Find full textW, Gellings Clark, and Barron W. L, eds. Demand forecasting for electric utilities. Lilburn, GA: Fairmont Press, 1992.
Find full textVeiderpass, Ann. Swedish retail electricity distribution: A non-parametric approach to efficiency and productivity change. Göteborg: [Gothenburg School of Economics?], 1993.
Find full textBook chapters on the topic "Electric power consumption – econometric models"
Stütz, Sebastian, Andreas Gade, and Daniela Kirsch. "Promoting Zero-Emission Urban Logistics: Efficient Use of Electric Trucks Through Intelligent Range Estimation." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 91–102. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_8.
Full textKulakova, Ekaterina, Vadim Kushnikov, Andrey Lazarev, and Inessa Borodich. "Models for Determining the Electric Power Consumption in the Water Recycling System at an Industrial Enterprise." In Recent Research in Control Engineering and Decision Making, 378–90. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65283-8_31.
Full textPleskach, Borys. "Estimation of Hidden Energy Losses." In Electric Power Conversion [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97504.
Full textAshok Shivarkar, Sandip, and Sandeep Malik. "A Survey on Electric Power Demand Forecasting." In Recent Trends in Intensive Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210236.
Full textVergara, Gonzalo, Juan J. Carrasco, Jesus Martínez-Gómez, Manuel Domínguez, José A. Gámez, and Emilio Soria-Olivas. "Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings." In Advances in Computer and Electrical Engineering, 207–34. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9911-3.ch011.
Full textVergara, Gonzalo, Juan J. Carrasco, Jesus Martínez-Gómez, Manuel Domínguez, José A. Gámez, and Emilio Soria-Olivas. "Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings." In Architecture and Design, 506–36. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7314-2.ch018.
Full textMado, Ismit. "Electric Load Forecasting an Application of Cluster Models Based on Double Seasonal Pattern Time Series Analysis." In Forecasting in Mathematics - Recent Advances, New Perspectives and Applications [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93493.
Full textPerez-Moscote, Daniel Adrian, and Mikhail Georgievich Tyagunov. "Improved Distributed Energy Systems Based on the End-User Consumption Profile." In Handbook of Research on Smart Technology Models for Business and Industry, 211–35. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3645-2.ch009.
Full textDhupia, Bhawna, and M. Usha Rani. "Assessment of Electric Consumption Forecast Using Machine Learning and Deep Learning Models for the Industrial Sector." In Advances in Wireless Technologies and Telecommunication, 206–18. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7685-4.ch016.
Full textCorcau, Jenica-Ileana, Liviu Dinca, and Ciprian-Marius Larco. "Modeling and Simulation of APU Based on PEMFC for More Electric Aircraft." In Aeronautics - New Advances [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105597.
Full textConference papers on the topic "Electric power consumption – econometric models"
Hobby, John D. "Constructing Demand Response Models for Electric Power Consumption." In 2010 1st IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2010. http://dx.doi.org/10.1109/smartgrid.2010.5622075.
Full textAbdelfattah, Eman, and Kevin Bowlyn. "Application of Machine Learning Models on Individual Household Electric Power Consumption." In 2023 IEEE World AI IoT Congress (AIIoT). IEEE, 2023. http://dx.doi.org/10.1109/aiiot58121.2023.10174456.
Full textKlavsuts, Irina L., Georgy L. Rusin, and Marina V. Khayrullina. "Strategic models of introducing innovative technology for management of electric power consumption into world markets." In 2016 13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). IEEE, 2016. http://dx.doi.org/10.1109/apeie.2016.7807064.
Full textCarlos Da Silva, Daniel, Laid Kefsi, and Antonio Sciarretta. "Analytical Models for the Sizing Optimization of Fuel Cell Hybrid Electric Vehicle Powertrains." In 16th International Conference on Engines & Vehicles. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-24-0133.
Full textLi, Candy Yuan, and Douglas Nelson. "Unified Net Willans Line Model for Estimating the Energy Consumption of Battery Electric Vehicles." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0348.
Full textDranuta, Diego, and Derek Johnson. "Analysis on Combined Heat and Power and Combined Heat and Power Hybrid Systems for Unconventional Drilling Operations." In ASME 2021 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/icef2021-67492.
Full textOzalp, Nesrin. "Utilization of Heat, Power and Recovered Waste Heat for Industrial Processes in the US Chemical Industry." In ASME 2008 2nd International Conference on Energy Sustainability collocated with the Heat Transfer, Fluids Engineering, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/es2008-54120.
Full textBriggs, Ian, Geoffrey McCullough, Stephen Spence, Roy Douglas, Richard O’Shaughnessy, Alister Hanna, Cedric Rouaud, and Rachel Seaman. "A Parametric Study of an Exhaust Recovery Turbogenerator on a Diesel-Electric Hybrid Bus." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94492.
Full textPodlaski, Meaghan, Farhan Gandhi, Robert Niemiec, and Luigi Vanfretti. "Multi-Domain Electric Drivetrain Modeling for UAM-Scale eVTOL Aircraft." In Vertical Flight Society 77th Annual Forum & Technology Display. The Vertical Flight Society, 2021. http://dx.doi.org/10.4050/f-0077-2021-16893.
Full textRakova, Elvira, and Jürgen Weber. "Exonomy Analysis for the Selection of the Most Cost-Effective Pneumatic Drive Solution." In 9th FPNI Ph.D. Symposium on Fluid Power. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/fpni2016-1518.
Full textReports on the topic "Electric power consumption – econometric models"
Haddad, J., L. A. Horta Nogueira, Germano Lambert-Torres, and L. E. Borges da Silva. Energy Efficiency and Smart Grids for Low Carbon and Green Growth in Brazil: Knowledge Sharing Forum on Development Experiences: Comparative Experiences of Korea and Latin America and the Caribbean. Inter-American Development Bank, June 2015. http://dx.doi.org/10.18235/0007001.
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