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

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.