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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wenyu Zhang Yuanyuan Wang Jianzhou Wang Jinzhao Liang |
| Copyright Year | 2009 |
| Abstract | Electricity demand forecasting is an important index to make power development plan and dispatch the loading of generating units in order to meet system demand. In order to improve the accuracy of the forecasting, we apply the feedforward neural network for electricity demand forecasting. Inspired by the idea of Artificial Fish Swarm Algorithm, in this paper we proposed one hybrid evolutionary algorithm which based on PSO and AFSA methods through crossing over the PSO and AFSA algorithms to train the feedforward neural network. This proposed method has been applied in a real electricity load forecasting, the results show that the proposed approach has a better generalization performance and is also more accurate and effective than the feedforward neural network trained by particle swarm optimization. |
| Starting Page | 98 |
| Ending Page | 102 |
| File Size | 297689 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424433346 |
| DOI | 10.1109/ICCET.2009.76 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-01-22 |
| Publisher Place | Singapore |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Demand forecasting Evolutionary computation Artificial neural networks Predictive models Multi-layer feedforward neural network Mathematics Artificial Fish Swarm Algorithm (AFSA) Feedforward neural networks Particle swarm optimization Power system modeling Load forecasting Neural networks Particle Swarm Optimization (PSO) |
| Content Type | Text |
| Resource Type | Article |
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