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| Research on Simulation of Short-term Power Load Forecasting based on Neural Network |
| Chen Ya1, Li Ping2 |
1. School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021;
2. Ningxia Key Laboratory of Intelligent Sensing for Desert Information, Yinchuan 750021 |
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Abstract Short-term load forecasting models based on BP neural network and Elman neural network are established in order to improve the accuracy of short-term power load forecasting.In order to improve the convergence rate, the BP neural network is optimized by the additional momentum method. For Elman neural network is easy to fall into the local extremum, so improve the incentive function and use the LM algorithm to optimize the learning algorithm.Matlab simulation results show that the improved Elman neural network model is better than the BP neural network model with high accuracy and fast convergence speed, which is more suitable for dynamic problems.
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Published: 20 January 2017
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| Cite this article: |
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Chen Ya,Li Ping. Research on Simulation of Short-term Power Load Forecasting based on Neural Network[J]. Electrical Engineering, 2017, 18(1): 26-29.
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| URL: |
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http://dqjs.cesmedia.cn/EN/Y2017/V18/I1/26
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2017, Vol. 18 
