基于子空间辨识方法的建筑群热负荷预测研究  

Heating load prediction of building complexes based on subspace identification method

作　　者：刘亚肖 王芃[1,2] 姜巍 Liu Yaxiao;Wang Peng;Jiang Wei(Harbin Institute of Technology,Harbin;Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology,Ministry of Industry and Information Technology,Harbin)

机构地区：[1]哈尔滨工业大学,哈尔滨150001 [2]寒地城乡人居环境科学与技术工业和信息化部重点实验室,哈尔滨

出　　处：《暖通空调》2024年第7期76-81,75,共7页Heating Ventilating & Air Conditioning

基　　金：国家重点研发计划“政府间国际科技创新合作”重点专项(编号:2021YFE0116100)。

摘　　要：根据建筑热力模型和热负荷计算模型建立了状态空间模型,分别利用未指定状态变量和指定状态变量的子空间辨识方法构建了建筑群热负荷预测模型,并以哈尔滨市某小区换热站为例,验证和比较了2种方法的热负荷预测结果及训练策略和预测时长对预测精度的影响。结果表明:指定状态变量比未指定状态变量的子空间辨识方法的热负荷预测平均绝对百分比误差降低了10.00%;未指定状态变量的子空间辨识方法的中期预测效果优于短期预测,平均绝对百分比误差降低了13.34%。The state space model is established according to the building thermodynamic model and the heating load calculation model.The heating load prediction model of building complexes is constructed by using the subspace identification method with unspecified and specified state variables,respectively.Taking a heat exchange station of a residential district in Harbin as an example,the heating load prediction results of the two methods and the effects of training strategies and prediction duration on the prediction accuracy are verified and compared.The results show that the mean absolute percentage error(MAPE)of the heating load prediction using the subspace identification method with specified state variables is reduced by 10.00%compared to that with unspecified state variables.The medium-term prediction effect of the subspace identification method with unspecified state variables is better than the short-term prediction effect,and the MAPE is reduced by 13.34%.

关 键 词：供热系统 热负荷预测 子空间辨识方法 建筑群 状态空间模型 状态变量 室内平均温度 

分 类 号：TU832.02[建筑科学—供热、供燃气、通风及空调工程]