Case study of Short Term Load Forecasting for weekends

Author

Salim, N.A. ; Rahman, T. K Abdul ; Jamaludin, M.F. ; Musa, M.F.

Author_Institution

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

fYear

2009

fDate

16-18 Nov. 2009

Firstpage

332

Lastpage

335

Abstract

This paper presents the short term load forecasting (STLF) to predict the demand in the future. STLF is a method used to predict a day ahead, 24 hours load demand. Two factors were considered in this forecasting: time and also the temperature of the day. The main objective of this project is to analyze the profile or pattern of the forecasted load and also to predict the load demand during weekends. Artificial neural network (ANN) in MATLAB software was used in solving the forecasting problem. The percentage of average error was determined by using the mean absolute percentage error (MAPE).

Keywords

load forecasting; neural nets; power engineering computing; ANN; MATLAB software; artificial neural network; average error; mean absolute percentage error; short term load forecasting; Artificial intelligence; Artificial neural networks; Biological neural networks; Demand forecasting; Economic forecasting; Humans; Load forecasting; Neurons; Power system planning; Temperature; Artificial Neural Network; Mean Absolute Percentage Error; Short Term Load forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Research and Development (SCOReD), 2009 IEEE Student Conference on

Conference_Location

UPM Serdang

Print_ISBN

978-1-4244-5186-9

Electronic_ISBN

978-1-4244-5187-6

Type

conf

DOI

10.1109/SCORED.2009.5443006

Filename

5443006