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Since the shortcomings of the BP neural network prediction method in predicting the building cooling load, such as slow convergence, easy to fall into local optimum and relatively low accuracy, the particle swarm optimization algorithm which has faster convergence and stronger global search capability is introduced to improve the prediction. By adding control error feedback parameters to the input parameters of the BP neural network structure, the BP neural network forecasting techniques based on particle swarm optimization and control error feedback is formed, applied in the central air conditioning system, and enhance the prediction accuracy.