Prediction of building demand cooling based on grey theory

The prediction of building demand cooling can help building operators understand the demand cooling of the building in advance, and make the optimal operation strategy of the air conditioning system for safe and energy saving operation. Based on the historical operation data of the intermittent air conditioning system of a complex building, studies the demand cooling prediction of the building in the morning using the grey prediction. Establishes several equal-dimension-new-information grey models to predict the morning demand cooling of different working days. Compares the predication accuracy of the GM(1,1) model only based on historical cooling capacity and the GM(1,2) model considering the effect of outdoor air temperature on demand cooling. The results show that the GM(1,2) model has a higher prediction accuracy than the GM(1,1) model by using the average outdoor air temperature as an independent variable. The grey prediction model can be integrated into the building management system (BMS) easily due to the simple model and less computation demand, and the predicted demand cooling can be used as a reference for the operation of the air conditioning system.