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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jun Chen Li He Yi Quan Wang Jiang |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Electr. & Electron. Eng., Hubei Univ. of Technol., Wuhan, China (Jun Chen; Li He; Yi Quan; Wang Jiang) |
| Abstract | BP Neural Network can forecast short-term electricity price, while it is necessary to explore technique to tune the back propagation learning algorithm either for better generalization, or for faster training. The paper proposed enhanced BP Neural Network to forecast electricity price, in which we replaced back propagation algorithm of BP Network with genetic simulated annealing algorithm (GSAA). It integrated GA's search performance and SA's strong local search performance, and has a better performance in terms of solution accuracy and convergence speed. Finally, a case study of New South Wales in Australia illustrates the feasibility and effectiveness of the proposed method. |
| Sponsorship | IEEE Madras Sect. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 278866 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479935437 |
| DOI | 10.1109/ICAEE.2014.6838562 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-01-09 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | BP Neural Network Electricity Neural networks Sociology Price forecasting Simulated annealing Predictive models Genetics Genetic simulated annealing algorithm (GSAA) Weight adjustment Forecasting |
| Content Type | Text |
| Resource Type | Article |
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