The neural network model based on PSO for short-term load forecasting

被引：0
|
作者：
Sun, Wei [1 ]
Zhang, Ying-Xia [1 ]
Li, Fang-Tao [2 ]
机构：
[1] North China Elect Power Univ, Dept Econ & Management, Baoding 071003, Peoples R China
[2] Jiangsu Huaneng Huaiyin Power Plan, Dept Engn, Huaiyin 223001, Peoples R China
来源：
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年
关键词：
load forecasting; particle swarm optimization; neural network; training algorithm;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new algorithm for load forecasting--the neural network model based on Particle Swarm Optimization (PSO-NN) for short-term load forecasting is proposed in this paper. The method is simple, easy to realize and its convergence rate is quick. The overall optimal solution of the problem can be found in great probability, and the intrinsic defects of artificial neural network, such as slow training speed and the existence of local minimum points, can be effectively overcome. Simulation results show that forecasting precision and speed can be improved by this method, and its forecasting capability is obviously better than the neural network model based on BP algorithm (BP-NN).
引用
收藏
页码：3069 / +
页数：2
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