District heating load patterns and short-term forecasting for buildings and city level

Pengmin Hua*, Haichao Wang, Zichan Xie, Risto Lahdelma

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)
112 Downloads (Pure)

Abstract

District heating (DH) load forecasting for buildings and cities is essential for DH production planning and demand-side management. This study analyzes and compares the hourly DH load patterns for a city and five different types of buildings over an entire year. The various operating modes introduce nonlinear dependencies between the DH load and the outdoor temperature. We compare the prediction accuracies of different multiple linear regression (MLR) and artificial neural network (ANN) models. Without nonlinear dependencies, both ANN and MLR provide good, almost identical prediction accuracies. In the case of nonlinear dependencies, ANN is superior to MLR. However, the novel clustering method eliminates nonlinear dependencies and improves the accuracy of MLR on par with the ANN. ANN methods can automatically adapt to various nonlinearities. The advantage of combining MLR with the clustering method is that it is simpler than designing an ANN method, although manual work is required. In addition, MLR methods provide more insight into load patterns and how the load depends on various factors compared with ‘black-box’ ANN models. The developed methodology can be widely applied to building- and city-level load analyses and forecasting in different DH systems combined with or without domestic hot water consumption.
Original languageEnglish
Article number129866
Number of pages13
JournalEnergy
Volume289
Early online date20 Dec 2023
DOIs
Publication statusPublished - 15 Feb 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Artificial neural networks
  • Buildings
  • City
  • Clustering method
  • District heat load forecasting
  • Multiple linear regression

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