基于分布特征学习和自适应哈里斯鹰算法的综合能源系统优化调度策略设计OA

Aiming at the problem that the accuracy and efficiency of traditional optimal scheduling methods are insufficient under complex conditions,an intelligent scheduling strategy based on distributed feature learning and adaptive Harris hawks optimization algorithm is presented.Distributed feature learning is used to deeply mine the operation rules in the historical data,and the adaptive mechanism and chaos factor are introduced into the Harris Hawks algorithm to achieve an effective balance between the global search ability and the local search ability.The experimental results show that the scheduling accuracy and efficiency of this strategy are more than 99%,which is significantly better than the comparison strategy,and the operation cost and energy consumption are effectively reduced,which lays a foundation for the safe and stable operation of the integrated energy system.