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A short-term prediction method of building energy consumption based on gradient progressive regression tree

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  • Qiuhong Zhao

Abstract

In order to overcome the large error of traditional methods in predicting building energy consumption, a short-term prediction method of building energy consumption based on gradient progressive regression tree is proposed. Building benchmark model is constructed by using eQuest software to obtain the main parameters affecting building energy consumption, build the impact index system of building energy consumption, and extract the main impact factors. Genetic algorithm is used to extract the characteristics of building energy consumption, combined with gradient progressive regression tree method to build a short-term prediction model of building energy consumption, and complete the short-term prediction of building energy consumption. The experimental results show that the minimum relative error of the proposed method is about 0.1, the absolute error is about 0.2, and the maximum standard deviation is 0.41.

Suggested Citation

  • Qiuhong Zhao, 2022. "A short-term prediction method of building energy consumption based on gradient progressive regression tree," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 44(2/3), pages 182-197.
    • Handle: RePEc:ids:ijgeni:v:44:y:2022:i:2/3:p:182-197
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    Cited by:

    1. Marcel Antal & Vlad Mihailescu & Tudor Cioara & Ionut Anghel, 2022. "Blockchain-Based Distributed Federated Learning in Smart Grid," Mathematics, MDPI, vol. 10(23), pages 1-19, November.

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