20.500.12556/RUL-124126 Machine-learning-based multi-step heat demand forecasting in a district heating system Short-term heat demand forecasting in district heating (DH) systems is essential for a sufficient heat supply and optimal operation of the DH. In this study, a machine learning based multi-step short-term heat demand forecasting approach using the data of the largest Slovenian DH system is considered. The proposed approach involved feature extraction and comparative analysis of different representative machine learning based forecasting models. Nonlinear models performed better than linear models, and the best forecasting results were obtained by Gaussian process regression (GPR), where the mean absolute normalized error was 2.94% of the maximum heating power of the DH system. The analysis confirmed the importance of accurate temperature forecasts but did not confirm the relevance of using future solar irradiation forecasts. The optimal length of training data is shown to be 3 years, and past data of up to 4 days can be used as input to increase the forecasting accuracy. The forecasting model (GPR) proposed in this study can be fitted to different DH systems. In the presented case study, it was selected to implement the online forecasting solution for the DH of Ljubljana and has been generating forecasts with a mean absolute normalized error of 2.70% since November 2019. district heating heat demand short-term forecasting machine learning Gaussian process regression daljinsko ogrevanje toplota kratkoročne napovedi strojno učenje Gaussova regresija procesa true false true Angleški jezik Slovenski jezik Članek v reviji 2021-01-04 15:05:51 2021-01-04 15:05:55 2022-09-04 03:57:50 0000-00-00 00:00:00 2021 0 0 Str. 1-14 Vol. 233 Feb. 2021 0000-00-00 PostprintKoncna Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 2023-01-04 697:536:004.85(045) 0378-7788 10.1016/j.enbuild.2020.110673 45195779 25395200 RAZ_Potocnik_Primoz_2021.pdf RAZ_Potocnik_Primoz_2021.pdf 1 2ADE9E1809C5093742DA40143B179320 fd087bb9be14c1ff110eef3e3403b102a773db0147bae1fa01a6b5e7687e45c8 7bd20b77-a1bb-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=139521 https://www.sciencedirect.com/science/article/pii/S0378778820334599?via%3Dihub 1 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=139520 Fakulteta za strojništvo 0 0 0