基于遗传算法的建筑热阻容模型辨识及室内温度预测  

Building thermal resistance-capacitance model identification and indoor temperature prediction based on genetic algorithm

作　　者：张欣林 曲明璐[1] 于震 李怀 罗翔 严旭峰 Zhang Xinlin;Qu Minglu;Yu Zhen;Li Huai;Luo Xiang;Yan Xufeng(University of Shanghai for Science and Technology,Shanghai;China Academy of Building Research,Beijing)

机构地区：[1]上海理工大学,上海 [2]中国建筑科学研究院有限公司,北京

出　　处：《暖通空调》2024年第10期80-85,158,共7页Heating Ventilating & Air Conditioning

基　　金：国家重点研发计划项目“高效柔性用能系统研究”(编号:2022YFC3801503)。

摘　　要：为了准确预测建筑物室内温度,采用集总参数法构建了建筑热阻容(RC)模型,并推导出了相应的微分方程组,使用遗传算法对模型进行了辨识。RC模型测试集温度平均绝对误差为0.14℃,R^(2)为0.99。比较了RC模型与2种黑箱模型对室内温度的预测结果,RC模型预测结果更准确。讨论了灰箱模型和黑箱模型的适用条件,建议根据需求和数据情况并考虑模型的复杂度、预测精度、解释性等因素,选择合适的建模方法。In order to accurately predict the indoor temperature of buildings,this study adopts the lumped parameter method to construct a building thermal resistance-capacitance(RC)model.The corresponding differential equations are derived,and the model is identified using the genetic algorithm.The RC model's test set indicates an average absolute error of temperature of 0.14℃and a R^(2) of 0.99.The prediction results of indoor temperature between the RC model and two black box models are compared,and the prediction results of the RC model are more accurate.This paper discusses the applicability conditions of gray box and black box models.It is recommended to select an appropriate modeling method based on the needs and data conditions,and considering the complexity,predictive accuracy,interpretability and other factors of the model.

关 键 词：室内温度预测 遗传算法 集总参数 热阻容模型 灰箱模型 黑箱模型 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TU111[自动化与计算机技术—控制科学与工程] TU831[建筑科学—建筑理论]