Title :
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
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;
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
DOI :
10.1109/SCORED.2009.5443006
