Optimization of Reservoir Operation Scheme Based on Fuzzy Optimization and Convolution Neural Network

被引:1
|
作者
Hu, He-xuan [1 ,6 ]
Miao, Yue-qing [1 ]
Hu, Qiang [1 ]
Zhang, Ye [1 ]
Hu, Zhen-yun [2 ]
Ma, Rui [3 ,4 ,5 ]
机构
[1] Hohai Univ, Coll Comp & Informat, Nanjing 211100, Peoples R China
[2] Hohai Univ, Business Sch, Nanjing 210098, Peoples R China
[3] Changjiang Survey Planning Design & Res Co Ltd, Wuhan 430010, Peoples R China
[4] Changjiang Spatial Informat Technol Engn Co Ltd, Wuhan 430010, Peoples R China
[5] Water Resources Informat Percept & Big Data Engn, Wuhan 430010, Peoples R China
[6] Tibet Agr & Anim Husb Coll, Sch Elect Engn, Nyingchi 860000, Tibet, Peoples R China
基金
国家重点研发计划;
关键词
Scheme Optimization; fuzzy optimization; CNN; AHP; entropy weight method;
D O I
10.1117/12.2601054
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The evaluation and optimization of reservoir operation schemes belong to a multi-objective, multi-level and multi-attribute decision-making problem. The reservoir multi-objective dispatching model generates many Pareto feasible solutions, and decision-makers often make decisions difficult. The traditional scheme selection method has the problems that the index weight is greatly affected by subjectivity, the single weight determination method is one-sided, the model calculation is complicated, and the characteristics of the evaluation index cannot be fully extracted. Therefore, this paper proposes a new method for reservoir operation plan optimization based on fuzzy optimization and convolutional neural network. First, establish the evaluation index system of the reservoir operation plan based on the fuzzy optimization theory, select the analytic hierarchy process to determine the subjective weight of the index, the entropy weight method to determine the objective weight, use the game theory to couple the subjective and objective weights, and calculate the comprehensive evaluation value of the plan through fuzzy comprehensive evaluation. Secondly, the evaluation index and comprehensive evaluation value are used as the input and output of the convolutional neural network to establish the optimal model of the reservoir operation plan. The results of the case analysis show that the research method has high accuracy and reliability, and can provide a scientific basis for reservoir operation decision-making.
引用
收藏
页数:11
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