Load prediction of primary heating networks based on tree models and neural networks

doi:10.3969/j.issn.2097-0706.2025.12.008

Abstract

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.

Key words: building energy consumption, load prediction, machine learning, tree model, neural network, correlation coefficient, building energy systems, heat supply

CLC Number:

TK89

YAN Jing, JIANG Zeling, GUAN Baoliang, MENG Siyu, WANG Fenglong, YANG Shangfeng, YANG Zhongyang, XIONG Yaxuan. Load prediction of primary heating networks based on tree models and neural networks[J]. Integrated Intelligent Energy, 2025, 47(12): 73-80.

Figures/Tables 8

Table 1

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Table 2

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Table 3

The evaluation index results of the models

模型	R_MSE/kW	M_AE/kW	M_APE/%	$R 2$
RFR	97.401 9	70.104 7	4.099 0	0.765 0
ETR	97.189 4	69.975 9	4.102 0	0.766 0
GBR	99.406 0	71.103 0	4.167 0	0.755 2
XGBR	97.800 3	69.967 1	4.086 0	0.763 1
MLP	103.872 6	73.294 7	4.271 0	0.732 7

Table 3

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参数	室外干球温度/℃	室外风速/(m·s^-1)	室外相对湿度/%	太阳辐照度/(W·m^-2)	热泵供水温度/℃	热泵回水温度/℃	热负荷/kW
最小值	-5.78	30.52	0.04	0	34.28	31.22	1 187.325 9
最大值	13.07	64.25	10.84	771.00	41.98	35.93	2 286.033 7
平均值	4.80	42.76	1.33	87.29	38.23	33.08	1 760.960 7