20.500.12556/DiRROS-9365
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
Ni določen
Neznano
2019-03-15 12:13:02
2019-03-15 12:13:02
2022-08-24 18:11:28
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
C04341262BA26D6B56B498819B93DEAA
f8e7247869ed5cb226b03a1f20e0ac09dc2873953c84a0a1f864d1ff7571cf4c
80ad5de4-17b5-11ed-b6b8-001a4af901a5
20.500.12556/dirros/82d7fd16-d452-4637-a40d-22e82e0ec4a0
https://dirros.openscience.si/Dokument.php?lang=slv&id=11222
Institut Jožef Stefan
0
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