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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Limin Huo Jinliang Yin Lirui Guo Jie Hu Xinqiao Fan |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Mech. & Electron. Eng., Agric. Univ. of Hebei, Baoding (Limin Huo; Jinliang Yin; Lirui Guo; Jie Hu; Xinqiao Fan) |
| Abstract | Short-term load forecasting is an important part of the modernized power system administration. Some methods have been applied to short-term load forecasting and obtained a certain achievement. Considering the characteristics of gene expression programming (GEP), it is possible to apply GEP to short-term load forecasting. But there still are some shortcomings of GEP. Such as the initial population that is generated randomly, mutation rate that can't be adjusted by itself and evolution result got before that can 't be utilized. In order to overcome these shortcomings, GEP was improved (IGEP) in the aspects of excessive multiplication, self-adaptive mutation rate and adopting mathematical model got before is proposed. And the IGEP was applied to short-term load forecasting. Firstly, the load series of the same time but different days are chosen as the training samples. Secondly, the load samples are filtered and processed generally. And finally, the short-term load is forecasted classified by weekday and weekend after eliminating the pseudo-data. Compared with the results forecasted by means of GP and GEP, it proves that the method of IGEP in short-term load forecasting is better. |
| Starting Page | 745 |
| Ending Page | 749 |
| File Size | 215567 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424417070 |
| DOI | 10.1109/ICCSC.2008.163 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-05-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Load forecasting Genetic mutations Genetic programming Tail Economic forecasting Power systems Agricultural engineering Gene expression Mathematical model Power engineering and energy |
| Content Type | Text |
| Resource Type | Article |
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