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A New Strategy for Short-Term Load Forecasting
Joint Authors
Yang, Yi
Wu, Jie
Li, Caihong
Chen, Yanhua
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-25
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yang, Yi& Wu, Jie& Chen, Yanhua& Li, Caihong. 2013. A New Strategy for Short-Term Load Forecasting. Abstract and Applied Analysis،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-454698
Modern Language Association (MLA)
Yang, Yi…[et al.]. A New Strategy for Short-Term Load Forecasting. Abstract and Applied Analysis No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-454698
American Medical Association (AMA)
Yang, Yi& Wu, Jie& Chen, Yanhua& Li, Caihong. A New Strategy for Short-Term Load Forecasting. Abstract and Applied Analysis. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-454698
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-454698