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  • A comparison of models for forecasting the residential natural gas demand of an urban area
    Hribar, Rok, 1988- ...
    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.
    Vir: Energy. - ISSN 0360-5442 (Vol. 167, Jan. 2019, str. 511-522)
    Vrsta gradiva - članek, sestavni del
    Leto - 2019
    Jezik - angleški
    COBISS.SI-ID - 31841575

    Povezava(-e):

    https://www.sciencedirect.com/science/article/pii/S0360544218321728?via%3Dihub
    DiRROS - Digitalni repozitorij raziskovalnih organizacij Slovenije
    Repozitorij Univerze v Ljubljani – RUL
    DOI

vir: Energy. - ISSN 0360-5442 (Vol. 167, Jan. 2019, str. 511-522)
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