Multi-criteria comprehensive study on predictive algorithm of heating energy consumption of district heating station based on timeseries processing

Chendong Wang, Jianjuan Yuan, Ji Zhang, Na Deng, Zhihua Zhou and Feng Gao

Energy, 2020, vol. 202, issue C

Abstract: Refined control of district heating station relies on reasonable and accurate prediction of heating energy consumption. Due to the influence of building thermal inertia and time-delay of the district heating system, certain research work is necessary to thoroughly illustrate and analyze the impact of data timeseries processing on various prediction models. Four kinds of prediction algorithms were investigated and compared in this paper. Results showed that all of three timeseries processing methods, namely time feature construction, sliding window and building thermal inertia coefficient (CBTI), can improve the prediction accuracy of all four models and CBTI had the greatest impact on model accuracy improvement. Moreover, timeseries processing method has no limitation on the types of prediction model and it is a general method to improve the model accuracy. Time feature construction and sliding window had greater influence on the non-neural network models while CBTI was the opposite. In terms of model robustness, the robustness had been significantly improved after introducing timeseries processing except random forest, and the comprehensive robustness coefficient for the other three models had been reduced by about 95%. Recurrent neural network had extremely excellent robustness under different temporal granularity.

Keywords: Heating energy consumption prediction; Data timeseries processing; Prediction accuracy; Prediction robustness; District heating station (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544220308215
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:202:y:2020:i:c:s0360544220308215

DOI: 10.1016/j.energy.2020.117714

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().