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Naslov:A comparison of models for forecasting the residential natural gas demand of an urban area
Avtorji:ID Hribar, Rok (Avtor)
ID Potočnik, Primož (Avtor)
ID Šilc, Jurij (Avtor)
ID Papa, Gregor (Avtor)
Datoteke:.pdf PDF - Predstavitvena datoteka, prenos (968,06 KB)
MD5: C04341262BA26D6B56B498819B93DEAA
PID: 20.500.12556/dirros/82d7fd16-d452-4637-a40d-22e82e0ec4a0
 
Jezik:Angleški jezik
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:Logo IJS - Institut Jožef Stefan
Povzetek: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.
Ključne besede:demand forecasting, buildings, energy modeling, forecast accuracy, machine learning
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Leto izida:2019
Št. strani:str. 511-522
Številčenje:Vol. 167
PID:20.500.12556/DiRROS-9365 Novo okno
UDK:004.9:620.9(045)
ISSN pri članku:0360-5442
DOI:10.1016/j.energy.2018.10.175 Novo okno
COBISS.SI-ID:31841575 Novo okno
Datum objave v DiRROS:15.03.2019
Število ogledov:2267
Število prenosov:1069
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Energy
Skrajšan naslov:Energy
Založnik:Pergamon Press
ISSN:0360-5442
COBISS.SI-ID:25394688 Novo okno

Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:P2-0098

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:P2-0241

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:PR-07606

Sekundarni jezik

Jezik:Ni določen
Ključne besede:napovedovanje odjema, zgradbe, energetsko modeliranje, natančnost napovedi, strojno učenje


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