Research on predicting heat loads based on extracting temporal and spatial features of multiple buildings using data-driven methods

Abstract

Accurate heat load prediction is the key to ensure the stable operation of thermal system and effective planning of thermal resources. However, current heat load forecasting methods tend to treat individual buildings as isolated entities, ignoring the temporal and spatial correlation between buildings. In this study, we propose a data-driven model based on spatiotemporal coupling to predict short-term heat load. Firstly, the spatial and temporal correlation and the correlation among features of various buildings are analyzed by using the autocorrelation function and Spearman correlation coefficient. Secondly, synchronous wavelet transform is used to eliminate high frequency noise of heat load. Secondly, the spatial and temporal characteristics of building heat load are extracted by using the improved Informer model which is concerned with multi-scale graphs. The experimental results show that the proposed model has better predictive performance than the baseline model. It provides a reference for the accurate regulation of thermal system.

KEYWORDS:

• Heat load prediction
• multi-scale graph attention mechanism
• improved informer
• synchronous wavelet transform
• spatio-temporal coupling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xue Guijun, Xie Wenju and Zhang Jingyi. The first draft of the manuscript was written by Tanquanwei and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Data availability statement

The datasets generated during and/or analysed during the current study are not publicly available due to data confidentiality but are available from the corresponding author on reasonable request.

Additional information

Funding

This study was supported by the Natural Science Foundation of Hebei Province (E2020209121).