20.500.12556/RUL-106552 A comparison of models for forecasting the residential natural gas demand of an urban area Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events. The models were trained and tested on gas-consumption data gathered in the city of Ljubljana, Slovenia. Machine-learning models were considered, such as linear regression, kernel machine and artificial neural network. Additionally, empirical models were developed based on data analysis. Two most accurate models were found to be recurrent neural network and linear regression model. In realistic setting such trained models can be used in conjunction with a weather-forecasting service to generate forecasts for future gas demand. demand forecasting buildings energy modeling forecast accuracy machine learning napovedovanje odjema zgradbe energetsko modeliranje natančnost napovedi strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2019-03-05 11:13:16 2019-03-05 11:13:17 2025-03-04 10:40:49 0000-00-00 00:00:00 2019 0 0 Str. 511-522 Vol. 167 Jan. 2019 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.9:620.9(045) 0360-5442 10.1016/j.energy.2018.10.175 31841575 25394688 RAZ_Hribar_Rok_i2019.pdf RAZ_Hribar_Rok_i2019.pdf 1 D646CD746C7C6E432CA7AB48725FCB82 789c7323a09b60f16420a1e3d6a529b80b0a0eaef0751b535b2dbbfbd8102143 5b2804c7-a1b6-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=117357 https://www.sciencedirect.com/science/article/pii/S0360544218321728?via%3Dihub 1 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=117356 Fakulteta za strojništvo 0 0 0