Chiller operation performance optimization using association rule algorithm

Proposes an association rule data mining method to optimize the performance of chillers. Taking the different rated power chiller system of a shopping mall in Guangzhou as a research object, based on the previous running data, analyses the association rules between the optimal operation energy efficiency and the running parameters of a single chiller under different operating conditions by Apriori frequent item set algorithm to guide the optimal operation of chiller system. The simulation results show that the energy consumption of the two different rated power chillers is 5.53% and 11.80% lower than that of the original operation mode in summer and winter respectively, with a remarkable energy saving effect, and verifies the validity of the algorithm. The rules are representative and practical, and can improve the energy saving potential of the chiller. This method is suitable for chillers with accumulated large amount of running data, which can provide reference for the energy saving and optimal operation of chillers.