Multi-granularity Autonomous Intelligent Method for Operation Optimization of Integrated Coal Mine Energy Systems

被引：0
作者：
Wang, Yan [1 ]
Gong, Dunwei [2 ]
Sun, Xiaoyan [1 ]
机构：
[1] China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou, Peoples R China
[2] Qingdao Univ Sci & Technol, Sch Informat Sci & Technol, Qingdao, Peoples R China
来源：
2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN | 2023年
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
Integrated coal mine energy system; Operation optimization; Adaptive selection; Autonomous intelligent optimization; Q-Learning; PARTICLE SWARM OPTIMIZATION; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; CONSTRAINTS; DISPATCH; DESIGN;
D O I：
10.1109/IJCNN54540.2023.10191698
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An intelligent optimization algorithm is only valid for solving some problems, which difficultly solves all operation optimization problems of integrated coal mine energy systems with different characteristics. An optimization paradigm usually consists of multiple operators/strategies, each of which is suitable for solving different problems. It is difficult for an operator/strategy to ensure that the population can evolve forward in the evolution process since the state of the population is changing. To this end, a multi-granularity autonomous intelligent optimization method is proposed to optimize the operation of integrated coal mine energy systems with various scenarios. This method automatically determines appropriate optimization paradigms according to problem characteristics and adaptively adjusts optimization operators/strategies based on population states in the evolution process. For the adaptive adjustment of operators/strategies, this paper proposes an adaptive adjustment strategy based on Q-Learning. Taking a coal mine in Shanxi Province, China as the research object, a series of experiments are conducted, and the experimental results show the effectiveness of the proposed algorithm.
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页数：10
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