Short-term Load forecasting by a new hybrid model

DOI

10.2991/ccis-13.2013.85How to use a DOI?

Keywords

short-term load forecasting; ARIMA; BP; hybrid model

Abstract

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. 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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guo Hehong
AU  - Du Guiqing
AU  - Wu Liping
AU  - Hu Zhiqiang
PY  - 2013/11
DA  - 2013/11
TI  - Short-term Load forecasting by a new hybrid model
BT  - Proceedings of the The 1st International Workshop on Cloud Computing and Information Security
PB  - Atlantis Press
SP  - 370
EP  - 374
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccis-13.2013.85
DO  - 10.2991/ccis-13.2013.85
ID  - Hehong2013/11
ER  -