Short-term energy consumption prediction method for educational buildings based on model integration

Wenqiang Cao, Junqi Yu, Mengyao Chao, Jingqi Wang, Siyuan Yang, Meng Zhou and Meng Wang

Energy, 2023, vol. 283, issue C

Abstract: Paying attention to the feature engineering problems is the basis for constructing a more accurate building energy consumption prediction model, which helps debug, control, and operate building energy management systems. Therefore, in this paper, an integrated energy consumption prediction model considering spatial characteristics in time series data is proposed to predict the short-term energy consumption of educational buildings, and the influence of features on the model is analyzed using the cooperative game theory SHAP method, and the optimal number of features is determined by ablation analysis. The proposed model is validated by an educational building in Xi'an, Shaanxi Province. The results show that compared with other energy consumption prediction models, the RMSE value of the integrated energy consumption prediction model is reduced by 13.64%–34.55%, and the MAE value is reduced by 10.25%–30.54%, which has higher prediction accuracy. In addition, this paper also investigates the minimum amount of data and the number of features required for the training of the building energy prediction model, and the integrated energy prediction model can still effectively predict building energy consumption when the training samples are minimal and the number of features is appropriate.

Keywords: Feature engineering; Short-term energy consumption; Integrated energy consumption prediction model; Ablation analysis; SHAP method (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223019746
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:283:y:2023:i:c:s0360544223019746

DOI: 10.1016/j.energy.2023.128580

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 ().