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

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

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.

• Jezik: Angleški jezik
• Ključne besede: district heating, heat demand, short-term forecasting, machine learning, Gaussian process regression
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Recenzirani rokopis
• Leto izida: 2021
• Št. strani: Str. 1-14
• Številčenje: Vol. 233
• PID: 20.500.12556/RUL-124126
• UDK: 697:536:004.85(045)
• ISSN pri članku: 0378-7788
• DOI: 10.1016/j.enbuild.2020.110673
• COBISS.SI-ID: 45195779
• Datum objave v RUL: 04.01.2021

Gradivo je del revije

• Naslov: Energy and buildings
• Skrajšan naslov: Energy build.
• Založnik: Elsevier
• ISSN: 0378-7788
• COBISS.SI-ID: 25395200

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: daljinsko ogrevanje, toplota, kratkoročne napovedi, strojno učenje, Gaussova regresija procesa

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0241
• Naslov: Sinergetika kompleksnih sistemov in procesov