Explanatory Optimization of the Prediction Model for Building Energy Consumption

Author:

Li Huiyu1ORCID,Dong Hailong1

Affiliation:

1. Department of Emergency Technology Management, Zhejiang College of Security Technology, Wenzhou 325006, China

Abstract

Traditional prediction models, which are based on artificial neural networks (ANNs), consider the various factors affecting building energy consumption comprehensively. However, their explanatory power is not ideal in actual application, resulting in prediction errors of building energy consumption. Thus, this paper pursues the explanatory optimization of the prediction model for building energy consumption. First, the authors displayed the architecture of the prediction model for building energy consumption, which is based on the temporal pattern attention mechanism (TPAM), and explained the principle of predicting building energy consumption. Then, the input of the TPAM was illustrated, and the execution steps of the model were depicted. Based on feature importance and the Shapley additive explanations (SHAP) method, the explanatory power of the proposed prediction model was analyzed, from the perspective of the time series features of building energy consumption prediction. The proposed model was proved effective through experiments.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards Sustainable Architecture: Machine Learning for Predicting Energy Use in Buildings;2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI);2024-05-09

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