The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing

被引:0
|
作者
Guo, Zhenwei [1 ,2 ]
Wang, Xinyu [2 ]
Wang, Yao [3 ,4 ]
Zhu, Fenglei [4 ]
Zhou, Haizhu [5 ]
Zhang, Miao [5 ]
Wang, Yuxiang [2 ]
机构
[1] State Key Lab Bldg Safety & Built Environm, Beijing 100013, Peoples R China
[2] Chinese Soc Urban Studies, Beijing, Peoples R China
[3] Tsinghua Univ, Sch Architecture, Beijing 100190, Peoples R China
[4] Beijing Glory PKPM Technol Co Ltd, Beijing 100013, Peoples R China
[5] China Acad Bldg Res, Beijing 100013, Peoples R China
关键词
building air conditioner load; machine learning; load control; energy costs; indoor temperature; ENERGY-CONSUMPTION; FAULT-DIAGNOSIS; DEMAND RESPONSE; RESERVE MARKET; COOLING SYSTEM; HVAC SYSTEM; PREDICTION; MODEL;
D O I
10.3390/buildings14103040
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.
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页数:18
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