Centrifugal chillers have been widely used in medium- and large-scale air conditioning projects. However, equipment running with faults will result in additional energy consumption. Meanwhile, it is difficult to diagnose the minor faults of the equipment. Therefore, the Extreme Gradient Boost (XGBoost) algorithm was used to solve the above problem in this article. The ASHRAE RP-1043 dataset was employed for research, utilizing the feature splitting principle of XGBoost to reduce the data dimension to 23 dimensions. Subsequently, the five important parameters of the XGBoost algorithm were optimized using Multi-swarm Cooperative Particle Swarm Optimization (MSPSO). The minor fault diagnosis model, MSPSO-XGBoost, was established. The results show that the ability of the proposed MSPSO-XGBoost model to diagnose eight different states is uniform, and the diagnostic accuracy of the model reaches 99.67%. The accuracy rate is significantly improved compared to that of the support vector machine (SVM) and back propagation neural network (BPNN) diagnostic models.

Dispatch optimization of the integrated energy system with multi-type energy storages, as an essential issue to optimum energy management, is a high-dimensional complex nonlinear problem with time-series constraints. To overcome the problem, a new meta-heuristic method is presented in this paper. The proposed method introduces several heuristic rules, called as maximum storage utilization heuristic rules, and embeds them into an adaptive GA to improve the quality of initial feasible solutions and new solutions in each iteration. In contrast to existing methods, the new method guarantees the convergence to an optimal solution with low computational cost. Simulation results indicate that the proposed method provides excellent performance in solving economic dispatch of integrated energy system with multi-type energy storages.