Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

Heat pump-based renewable energy and waste heat recycling has become the mainstay of the Clean Heating Plan. Still, configuring a fit control system for that remains a worthwhile research topic. In order to address the problems that arose in the previous heating period, this study configures a Smith-predictor-based PID cascade control system with an advanced fireworks algorithm that adaptively tunes the structural parameters of controllers. Simulation and live measures demonstrate that the upgraded control scheme counters the adverse effects of time lag, reduces overshoot, and shortens the settling time. Further, benefiting from a delicate balance between heating demand and supply, the heating system with upgraded management increases the average exergetic efficiency by 11.4% and decreases the complaint rate by 76.5%. Note that the advanced fireworks algorithm mitigates the adverse effect of capacity lag and simultaneously accelerates the optimizing and converging processes, exhibiting its comprehensive competitiveness among this study’s three intelligent optimization algorithms. Nonetheless, the forecast and regulation of the return water temperature of the heating system are independent of each other in this research. Configuring an integral predictive control structure for the return water temperature of heating systems is worthwhile in the future.