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Machine-learning-based multi-step heat demand forecasting in a district heating system
Potočnik, Primož (Author), Škerl, Primož (Author), Govekar, Edvard (Author)

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Abstract
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.

Language:English
Keywords:district heating, heat demand, short-term forecasting, machine learning, Gaussian process regression
Work type:Article (dk_c)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Year:2021
Number of pages:Str. 1-14
Numbering:Vol. 233
UDC:697:536:004.85(045)
ISSN on article:0378-7788
DOI:10.1016/j.enbuild.2020.110673 This link opens in a new window
COBISS.SI-ID:45195779 This link opens in a new window
Views:120
Downloads:71
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Record is a part of a journal

Title:Energy and buildings
Shortened title:Energy build.
Publisher:Elsevier
ISSN:0378-7788
COBISS.SI-ID:25395200 This link opens in a new window

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:P2-0241
Name:Sinergetika kompleksnih sistemov in procesov

Secondary language

Language:Slovenian
Keywords:daljinsko ogrevanje, toplota, kratkoročne napovedi, strojno učenje, Gaussova regresija procesa

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