Research on Optimal Control Strategy of Chiller System Based on Deep Learning and Grid Search
Chiller systems account for a huge energy consumption.To decoupling the variables of chillers with variable frequency compressor,pumps and fan,the paper uses deep neural networks to establish a multi-layer decoupling model.The real-time operating data of chillers from a factory in Ningbo in the past two years was collected as the training set and test set of the models.The prediction of chilled water return temperature,the frequency of cooling water pumps and chilled water pumps is achieved through environment variables and related control variables on the first stage.On this basis,the second stage we further establish a chiller power consumption prediction model.Finally,the optimal control parameters are determined by the grid search method.After testing,the average relative errors of the energy consumption prediction model are 4.61%.On a typical day,the energy consumption of the chiller under the optimized strategy operation was reduced by 9.96%.
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