Application of Chaos and Neural Network in Power Load Forecasting
This paper employs chaos theory into power load forecasting. Lyapunov exponents on chaos theory are calculated to judge whether it is a chaotic system. Delay time and embedding dimension are calculated to reconstruct the phase space and determine the structure of artificial neural network (ANN). Imp...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2011/597634 |
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| _version_ | 1832565242469548032 |
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| author | Li Li Liu Chong-xin |
| author_facet | Li Li Liu Chong-xin |
| author_sort | Li Li |
| collection | DOAJ |
| description | This paper employs chaos theory into power load forecasting. Lyapunov exponents on chaos theory are calculated to judge whether it is a chaotic system. Delay time and embedding dimension are calculated to reconstruct the phase space and determine the structure of artificial neural network (ANN). Improved back propagation (BP) algorithm based on genetic algorithm (GA) is used to train and forecast. Finally, this paper uses the load data of Shaanxi province power grid of China to complete the short-term load forecasting. The results show that the model in this paper is more effective than classical standard BP neural network model. |
| format | Article |
| id | doaj-art-7773e1f7bf0042e7b32041e2dd819316 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-7773e1f7bf0042e7b32041e2dd8193162025-02-03T01:08:56ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2011-01-01201110.1155/2011/597634597634Application of Chaos and Neural Network in Power Load ForecastingLi Li0Liu Chong-xin1School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, ChinaSchool of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, ChinaThis paper employs chaos theory into power load forecasting. Lyapunov exponents on chaos theory are calculated to judge whether it is a chaotic system. Delay time and embedding dimension are calculated to reconstruct the phase space and determine the structure of artificial neural network (ANN). Improved back propagation (BP) algorithm based on genetic algorithm (GA) is used to train and forecast. Finally, this paper uses the load data of Shaanxi province power grid of China to complete the short-term load forecasting. The results show that the model in this paper is more effective than classical standard BP neural network model.http://dx.doi.org/10.1155/2011/597634 |
| spellingShingle | Li Li Liu Chong-xin Application of Chaos and Neural Network in Power Load Forecasting Discrete Dynamics in Nature and Society |
| title | Application of Chaos and Neural Network in Power Load Forecasting |
| title_full | Application of Chaos and Neural Network in Power Load Forecasting |
| title_fullStr | Application of Chaos and Neural Network in Power Load Forecasting |
| title_full_unstemmed | Application of Chaos and Neural Network in Power Load Forecasting |
| title_short | Application of Chaos and Neural Network in Power Load Forecasting |
| title_sort | application of chaos and neural network in power load forecasting |
| url | http://dx.doi.org/10.1155/2011/597634 |
| work_keys_str_mv | AT lili applicationofchaosandneuralnetworkinpowerloadforecasting AT liuchongxin applicationofchaosandneuralnetworkinpowerloadforecasting |