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Short term load forecasting using improved particle swarm fuzzy neural network

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成果类型:
期刊论文
作者:
Zhao, Yuhong;Hu, Heping;Hong, Zhennan
通讯作者:
Zhao, Y.
作者机构:
[Hong, Zhennan; Zhao, Yuhong] School of Electric Engineering, University of south China, Hengyang, 421001, Hunan, China
[Hu, Heping] School of Mathematics and Physics, University of south China, Hengyang, 421001, Hunan, China
通讯机构:
[Zhao, Y.] S
School of Electric Engineering, , Hengyang, 421001, Hunan, China
语种:
英文
关键词:
Fuzzy;Improved particle swarm optimization;Neural network;Short-term load forecasting
期刊:
International Journal of Applied Mathematics & Statistics
ISSN:
0973-1377
年:
2013
卷:
41
期:
11
页码:
246-254
机构署名:
本校为第一机构
院系归属:
电气工程学院
数理学院
摘要:
The development of electricity market requires more accurate short-term load forecasting (STLF). A novel bionic algorithm is proposed to improve the STLF accuracy and speed. In this paper, improved particle swarm algorithm (IPSO), fuzzy theory (Fuzzy) and BP neural network (BPNN) are combined to form a new STLF method which is called IPSO-F-BPNN. When we build this model, the impact of climate and temperature is processed with fuzzy technique and considered as input data of the network. The improved particle swarm algorithm is used to train network parameters until the learning error tends to ...

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