Open Access
2013 A New Strategy for Short-Term Load Forecasting
Yi Yang, Jie Wu, Yanhua Chen, Caihong Li
Abstr. Appl. Anal. 2013(SI14): 1-9 (2013). DOI: 10.1155/2013/208964


Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF) plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.


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Yi Yang. Jie Wu. Yanhua Chen. Caihong Li. "A New Strategy for Short-Term Load Forecasting." Abstr. Appl. Anal. 2013 (SI14) 1 - 9, 2013.


Published: 2013
First available in Project Euclid: 26 February 2014

Digital Object Identifier: 10.1155/2013/208964

Rights: Copyright © 2013 Hindawi

Vol.2013 • No. SI14 • 2013
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