Operating optimization of air-conditioning water system in a subway station using data mining and dynamic system models

Energy-conservation potential in the air-condition water system for subway stations is huge due to its conservative design method. Also, for operation strategy of such systems, the operation modes are formulated with the fixed schedule. This paper presents a data-based optimization method to obtain optimal parameters of the system for feedforward control. The data mining models are established by using the data from energy consumption platform of the refrigerating system. The study utilized the box-plot method, kNN algorithm and k-means algorithm to process and repair original data. Then Artificial Neural Network (ANN) model is adopted to developed the forecasting model to assess load, performance and energy consumption of the system. The input features of the models are determined by the existed models and clustering analysis. The optimal parameters under the conditions of different load-ratio range and ambient thermal environments are calculated via Genetic Algorithm and trained equipment models. And the optimal parameters are applied to establish operation schedule based on feedforward control and response time. The optimal feedforward control method is verified by a validated TRNSYS model. When the parameters are optimized, the water system energy consumption can be save by 9.5% in a cooling season.