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A comparison of models for forecasting the residential natural gas demand of an urban area
Hribar, Rok (Avtor), Potočnik, Primož (Avtor), Šilc, Jurij (Avtor), Papa, Gregor (Avtor)

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Izvleček
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.

Jezik:Angleški jezik
Ključne besede:demand forecasting, buildings, energy modeling, forecast accuracy, machine learning
Vrsta gradiva:Članek v reviji (dk_c)
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Leto izida:2019
Št. strani:str. 511-522
Številčenje:Vol. 167
UDK:004.9:620.9(045)
ISSN pri članku:0360-5442
DOI:10.1016/j.energy.2018.10.175 Povezava se odpre v novem oknu
COBISS.SI-ID:31841575 Povezava se odpre v novem oknu
Število ogledov:57
Število prenosov:101
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
Program financ.:Raziskovalni projekti - aplikativni
Številka projekta:P2-0098, P2-0241, PR-07606
Naslov:Računalniške strukture in sistemi, Sinergetika kompleksnih sistemov in procesov
Akronim:
ID projekta:info:eu-repo/grantAgreement/ARRS/Raziskovalni%20projekti%20-%20aplikativni/P2-0098%2C%20P2-0241%2C%20PR-07606

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:napovedovanje odjema, zgradbe, energetsko modeliranje, natančnost napovedi, strojno učenje

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