Very Short-Term Load Forecasting Based on Neural Network and Rough Set
The short-term load forecasting model based on neural network has been applied widely in energy management systems (EMS) because of its high forecasting accuracy and self-learning ability. But the forecasting errors of the load curve near peaks are large, especially at the large slope difference on...
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| Vydáno v: | 2010 International Conference on Intelligent Computation Technology and Automation Ročník 3; s. 1132 - 1135 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
01.05.2010
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| ISBN: | 9781424472796, 1424472792 |
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| Abstract | The short-term load forecasting model based on neural network has been applied widely in energy management systems (EMS) because of its high forecasting accuracy and self-learning ability. But the forecasting errors of the load curve near peaks are large, especially at the large slope difference on both side of a peak. So the load forecasting based on rough set and neural network is proposed. The load in the current time interval, load in the previous time interval, load deviation between the current time interval and the previous time interval and current time is regarded as an input of a neural network respectively. The forecasting load at following time interval is the output of the neural network. The trained neural network is the load forecasting model based on neural network. Then, the forecasting load at following time interval obtained by the neural network based load forecasting model is compensated by rough set to increase the forecasting accuracy. The simulation experiments show that the presented load forecasting based on rough set and neural network can improve the forecasting accuracy significantly. |
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| AbstractList | The short-term load forecasting model based on neural network has been applied widely in energy management systems (EMS) because of its high forecasting accuracy and self-learning ability. But the forecasting errors of the load curve near peaks are large, especially at the large slope difference on both side of a peak. So the load forecasting based on rough set and neural network is proposed. The load in the current time interval, load in the previous time interval, load deviation between the current time interval and the previous time interval and current time is regarded as an input of a neural network respectively. The forecasting load at following time interval is the output of the neural network. The trained neural network is the load forecasting model based on neural network. Then, the forecasting load at following time interval obtained by the neural network based load forecasting model is compensated by rough set to increase the forecasting accuracy. The simulation experiments show that the presented load forecasting based on rough set and neural network can improve the forecasting accuracy significantly. |
| Author | Pang Qingle Zhang Min |
| Author_xml | – sequence: 1 surname: Pang Qingle fullname: Pang Qingle email: stefam@163.com organization: Sch. of Inf. & Electron. Eng., Shandong Inst. of Bus. & Technol., Yantai, China – sequence: 2 surname: Zhang Min fullname: Zhang Min email: zh-dewei@163.com organization: Coll. of Comput. Sci., Liaocheng Univ., Liaocheng, China |
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| Snippet | The short-term load forecasting model based on neural network has been applied widely in energy management systems (EMS) because of its high forecasting... |
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| SubjectTerms | Artificial neural networks Autoregressive processes Economic forecasting Load forecasting Load modeling Medical services Neural Network Neural networks Power system modeling Power system reliability Predictive models Rough Set Very Short-Term Load Forecasting |
| Title | Very Short-Term Load Forecasting Based on Neural Network and Rough Set |
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