综合智慧能源2025，Vol.47Issue(12)：73-80,8.DOI:10.3969/j.issn.2097-0706.2025.12.008
基于树模型和神经网络的供热一次网负荷预测
Load prediction of primary heating networks based on tree models and neural networks
摘要
Abstract
Accurate prediction of building heating load is crucial for optimizing the operation of building energy systems,reducing energy consumption,and achieving building energy-saving goals.Using machine learning algorithms for prediction can effectively overcome the limitations of traditional prediction methods,significantly reducing the computational cost of conventional simulation analyses and enhancing system energy efficiency.Five regression machine learning models—random forest regression(RFR),extremely randomized trees regression(ETR),gradient boosting regression(GBR),extreme gradient boosting regression(XGBR),and multilayer perceptron(MLP)—were employed to predict building heating load.Four indicators were used to evaluate their prediction accuracy.The results showed that the ETR and XGBR models demonstrated the optimal predictive performance among all models.The ETR model achieved a root mean square error(RMSE)as low as 97.189 4 kW and an R2 value of 0.766 0.The XGBR model achieved a mean absolute error(MAE)and a mean absolute percentage error(MAPE)as low as 69.967 1 kW and 4.086 0%,respectively.These two models achieve high predictive accuracy,providing valuable references for subsequent research on building heating load prediction.关键词
建筑能耗/负荷预测/机器学习/树模型/神经网络/相关系数/建筑能源系统/供热Key words
building energy consumption/load prediction/machine learning/tree model/neural network/correlation coefficient/building energy systems/heat supply分类
能源科技引用本文复制引用
闫京,蒋泽龄,关宝良,孟思宇,王凤龙,杨尚峰,杨众杨,熊亚选..基于树模型和神经网络的供热一次网负荷预测[J].综合智慧能源,2025,47(12):73-80,8.基金项目
国家重点研发计划项目(2025YFE0118800)National Key R&D Program of China(2025YFE0118800) （2025YFE0118800）
