基于多目标回归的空调负荷预测方法

Forecasting method of air conditioning load based on multi-objective regression

针对空调为二次泵变流量系统时,考虑分区域供冷工况下,采用多目标回归方式解决负荷预测问题将有利于提高负荷预测准确性的情况,提出了两种多目标回归的中央空调负荷预测模型,即多目标支持向量回归(support vector regression,SVR)负荷预测模型和多目标长短期记忆(long short term memory,LSTM)神经网络负荷预测模型,利用上海市某医院的二次泵变流量系统数据对两个模型进行训练和预测,并与单目标回归预测模型进行比较.研究结果表明:相较单目标回归预测模型,两种多目标预测模型的预测精度更高;多目标SVR负荷预测模型较多目标LSTM负荷预测模型的预测准确性更高.

For the air conditioning with secondary pump variable flow system,considering the regional cooling situation,the multi-objective regression method was used to solve the load forecasting problem for improving the accuracy of load forecasting.For the central air conditioning,two multi-objective regression load forecasting models of multi-objective support vector regression(SVR)and multi-objective long short-term memory(LSTM)neural network were proposed.The two models were used to train and predict on the data of the secondary pump variable flow system of the hospital in Shanghai,and the results were compared with those of the single objective regression prediction model.The results show that the prediction accuracies of the two multi-objective prediction models are higher than that of the single objective regression prediction model,and the multi-objective SVR load forecasting model has higher prediction accuracy than the multi-objective LSTM load forecasting model.