A Hybrid Method Of Cooling Load Forecasting For Large Commercial Building Based On Extreme Learning Machine

Air conditioning system is extensively used in large commercial buildings. The fast and accurate building cooling load forecasting is the basis for improving the operation efficiency of the air conditioning system, which is conducive to implement the effective management of the air conditioning system. Therefore, a hybrid prediction model based on random forest-improvement parallel whale optimizing-extreme learning machine neural network (RF-IPWOA-ELM) is proposed to predict the cooling load of large commercial buildings. First, the influence of different parameters on the cooling load is analyzed, and the random forest (RF) method is used to extract the parameters with high degree of influence as the input variables of prediction model. Then, the extreme learning machine (ELM) optimized by the improved parallel whale optimization algorithm (IPWOA) is established to predict. Finally, a simulation experiment is carried out using measured data of two large commercial buildings in north of China. The experimental results show that the root mean square error (RMSE) and mean average percentage error (MAPE) of RFIPWOA-ELM predicting the cooling load for these two buildings are 2.8735, 0.2% and 4.7721, 0.45%, respectively. Compared with the other prediction model, the RMSE and MAPE of this model are reduced by 66.17%-90.62%, 81.48%-95.79% and 71.91%-84.40%, 74.14%-86.15%, respectively, which has higher prediction accuracy. Simultaneously, for different prediction models, RF-IPWOA-ELM has a shorter prediction time, which presents superiority in time complexity. And when there are few training samples, RF-IPWOA-ELM can still effectively predict the cooling load in different months, indicating that it possesses strong generalization ability. Therefore, the proposed hybrid model can be used as a reliable tool for cooling load prediction in the energy conservation of air conditioning system and energy management. (c) 2021 Elsevier Ltd. All rights reserved.