Short term load forecasting using artificial neural network with feature extraction method and stationary output

Author

Othman, M.M. ; Harun, M.H.H. ; Musirin, I.

Author_Institution

Fac. of Electr. Eng., Univ. Teknol. MARA, Shah Alam, Malaysia

fYear

2012

fDate

6-7 June 2012

Firstpage

480

Lastpage

484

Abstract

This paper presents the artificial neural network (ANN) that used to perform STLF for the next 24 hours. The feature extraction involves a transformation of raw data that is from the chronological hourly peak loads to the multiple time lags of hourly peak loads. This is used to improve the input data which will significantly enhance the performance of ANN in forecasting the hourly peak loads with less error. The output of ANN is then converted to a non-stationary form which represents as the forecasted hourly peak load for the next 24 hour. The Malaysian hourly peak loads in the year 2002 is used as case study to verify the effectiveness of ANN in STLF.

Keywords

feature extraction; load forecasting; neural nets; power engineering computing; ANN output; ANN performance; STLF; artificial neural network; chronological hourly peak loads; feature extraction method; multiple time lags; raw data transformation; short term load forecasting; stationary output; Artificial neural networks; Feature extraction; Forecasting; Load forecasting; Time series analysis; Artificial neural network (ANN); multiple time lags; short-term load forecasting (STLF); stationary ANN output;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering and Optimization Conference (PEDCO) Melaka, Malaysia, 2012 Ieee International

Conference_Location

Melaka

Print_ISBN

978-1-4673-0660-7

Type

conf

DOI

10.1109/PEOCO.2012.6230912

Filename

6230912