Forecasting electricity consumption of OECD countries: A global machine learning modeling approach

Sen, DorukTunc, K. M. MuratGunay, M. Erdem2024-07-182024-07-1820210957-17871878-4356https://doi.org/10.1016/j.jup.2021.101222https://hdl.handle.net/11411/8988Electricity is a critical utility for social growth. Accurate estimation of its consumption plays a vital role in economic development. A database that included past electricity consumption data from all OECD countries was prepared. Since national trends may be transferable from one country to another, the entire database was modeled and simulated via machine learning techniques to forecast the energy consumption of each country. Understanding similarities among the profiles of different countries could increase predictive accuracy and improve associated public policies.eninfo:eu-repo/semantics/closedAccessMachine LearningArtificial Neural NetworkSupport Vector MachineNatural-Gas ConsumptionEnergy-ConsumptionTime-SeriesSocioeconomic IndicatorsNeural-NetworkDemandCombinationRegressionArimaForecasting electricity consumption of OECD countries: A global machine learning modeling approachArticle2-s2.0-8510554656810.1016/j.jup.2021.101222Q170Q3WOS:000658799200003