Predicting Cooling Loads for the Next 24 Hours Based on General Regression Neural Network: Methods and Results

摘要

Predicting cooling load for the next 24 hours is essential for the optimal control of air-conditioning systems that use thermal cool storage. This study investigated modeling methods of applying the general regression neural network (GRNN) technology to predict load. The single stage (SS) and double stage (DS) prediction methods were introduced. Two SS and two DS models were set up for forecasting the next 24 hours' cooling load. Measured data collected from two five star hotels located in Sanya, China, were used to train and test these models. The results demonstrate that the SS method, which can eliminate the necessity for measuring and predicting meteorological data, is much simpler and reliable for predicting the cooling load in practical applications.