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The application of improved genetic algorithm optimized by radial basis function in electric power system

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成果类型:
期刊论文、会议论文
作者:
Yuhong Zhao;Heguo Hu;Yunhui Zhang
通讯作者:
Zhao, Y.(gsxl666@163.com)
作者机构:
[Zhao Y.] School of Electric Engineering, University of South China, Hunan, China
[Zhang Y.] Eastern Boiler Control Company Limited, Shenzhen, China
[Heguo Hu] Statistical Bureau of Hengyang City, Hunan, China
语种:
英文
关键词:
Electric load forecasting;Electric power systems;Genetic algorithms;Radial basis function networks;Adaptive crossover and mutation probabilities;Gradient Descent method;Load forecasting;Power system operations;Pre-mature convergences;Radial basis function neural networks;Radial basis functions;Short term load forecasting;Iterative methods
期刊:
Lecture Notes in Electrical Engineering
ISSN:
1876-1100
年:
2014
卷:
237 LNEE
页码:
325-333
会议名称:
2013 International Conference on Mechatronics and Automatic Control Systems, ICMS 2013
会议论文集名称:
Mechatronics and Automatic Control Systems
会议时间:
10 August 2013 through 11 August 2013
会议地点:
Hangzhou
主编:
Wego Wang
出版者:
Springer Verlag
ISBN:
9783319012728
基金类别:
A Project Supported by Scientific Research Fund of Hunan Provincial Science and Technology Programme, Project Number [2010FJ3157]
机构署名:
本校为第一机构
院系归属:
电气工程学院
摘要:
This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions; therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. In order to improve the precision of electric power system load forecasting, the hybrid algorithm which combines improved genetic algorithm with radial basis function (RBF) neural network is used in short-term load forecasting of electric powe...

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