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
Hlavní autoři: Pang Qingle, Zhang Min
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2010
Témata:
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
ISBN:9781424472796, 1424472792
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Shrnutí: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.
ISBN:9781424472796
1424472792
DOI:10.1109/ICICTA.2010.38