Machine-learning-based multi-step heat demand forecasting in a district heating system

Primož Potočnik (Author), Primož Škerl (Author), Edvard Govekar (Author)

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.

Keywords

district heating;heat demand;short-term forecasting;machine learning;Gaussian process regression;

Data

Language:	English
Year of publishing:	2021
Typology:	1.01 - Original Scientific Article
Organization:	UL FS - Faculty of Mechanical Engineering
UDC:	697:536:004.85(045)
COBISS:	45195779
ISSN:	0378-7788
Views:	376
Downloads:	86
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	daljinsko ogrevanje;toplota;kratkoročne napovedi;strojno učenje;Gaussova regresija procesa;
Type (COBISS):	Article
Embargo end date (OpenAIRE):	2023-01-04
Pages:	str. 1-14
Issue:	ǂVol. ǂ233
Chronology:	Feb. 2021
DOI:	10.1016/j.enbuild.2020.110673
ID:	12349065