| 引用本文: | 刘巍,李锰,李秋燕,等.基于改进遗传算法的电网投资组合预测方法[J].电力系统保护与控制,2020,48(8):78-85. |
| LIU Wei,LI Meng,LI Qiuyan,et al.Power grid portfolio forecasting method based on an improved genetic algorithm[J].Power System Protection and Control,2020,48(8):78-85 |
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| 基于改进遗传算法的电网投资组合预测方法 |
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刘巍1,李 锰2,李秋燕2,王利利2,胡 钋3,凌汝晨4,高玉芹3,李 智3
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(1.国网河南省电力公司，河南 郑州 450000;2.国网河南省电力公司经济技术研究院，河南 郑州 450052; 3.武汉大学电气工程学院，湖北 武汉 430072;4.国网浙江省电力公司嘉兴供电公司，浙江 嘉兴 314000)
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| 摘要: |
| 提出了一种通过改进遗传算法并综合利用灰色预测GM(1, N)模型、BP神经网络模型、多元回归模型建立的电网投资组合预测模型。基于传统遗传算法对组合预测约束条件进行了优化并改进了遗传算法中交叉算子和变异算子，从而使算法具有更强的全局搜索能力和收敛能力。利用所提出的组合预测模型对某地区电网投资进行预测的结果表明，相比于单一预测模型和其他两种组合预测模型，所提组合预测模型能充分利用原始数据的信息，具有更高的预测精度。 |
| 关键词: 灰色预测 BP神经网络 多元回归 遗传算法 组合预测 |
| DOI：10.19783/j.cnki.pspc.190644 |
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| 基金项目:国家高技术研究发展计划(863计划)(2015 AA050101);国网河南省电力公司科技项目(5217L017000X) |
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| Power grid portfolio forecasting method based on an improved genetic algorithm |
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LIU Wei1,LI Meng2,LI Qiuyan2,WANG Lili2,HU Po3,LING Ruchen4,GAO Yuqin3,LI Zhi3
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(1. State Grid Henan Electric Power Company, Zhengzhou 450000, China;2. State Grid Henan Economic Research Institute, Zhengzhou 450052, China;3. School of Electrical Engineering, Wuhan University, Wuhan 430072, China;4. Jiaxing Power Supply Company, State Grid Zhejiang Electric Power Company, Jiaxing 314000, China)
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| Abstract: |
| This paper proposes a grid portfolio forecasting model established by an improved genetic algorithm and comprehensively using the grey prediction GM (1, N), BP neural network and multiple regression models. The traditional genetic algorithm is used to optimize the combined prediction constraints and improve the crossover operator and mutation operator in the genetic algorithm, so that the algorithm has stronger global search and convergence ability. The prediction results of a regional grid investment using the combined forecasting model proposed in this paper show that compared with the single forecasting model and the other two combined forecasting models, the proposed model can make full use of the original data and has higher prediction accuracy. This work is supported by National High-tech R & D Program of China (863 Program) (No. 2015AA050101). |
| Key words: grey prediction BP neural network multiple regression genetic algorithm combined forecast |
