制冷技术2024，Vol.44Issue(5)：9-15,7.DOI:10.3969/j.issn.2095-4468.2024.05.102
基于梯度提升回归树负荷预测的水蓄冷空调系统运行优化
Operation Optimization for Chilled Water Storage Air Conditioning System Based on Load Prediction Using Gradient Boosting Regression Tree
摘要
Abstract
In this paper,an operation optimization strategy for water storage air conditioning system based on hyperparameter optimization load prediction is proposed.The Gradient Boosting Regression Tree algorithm is used to establish the load prediction model,and the Tree-structured Parzen Estimator algorithm based on Bayesian optimization is used for hyperparameter optimization.Based on the load prediction results,the start-stop combination of the chillers and the charging/discharging capacity are optimized,to optimize the operation of the water storage air conditioning system.Taking the air conditioning system of a factory as the research object,a water storage system is added,and the proposed optimization operation strategy is verified.Compared with the original system,the average daily performance coefficients of the three typical days of high,medium and low load are increased by 4.84%,3.55%and 2.67%,respectively,and the energy saving rates are 4.60%,3.14%and 2.48%,respectively.关键词
水蓄冷/负荷预测/超参数优化/运行优化Key words
Water storage/Load prediction/Hyperparameter optimization/Operation optimization分类
通用工业技术引用本文复制引用
李玲荣,贾志洋,薛琪,吕远,王江情,晋欣桥..基于梯度提升回归树负荷预测的水蓄冷空调系统运行优化[J].制冷技术,2024,44(5):9-15,7.基金项目
国家自然科学基金(No.51776118). （No.51776118）
