Health & Environmental Research Online (HERO)


Print Feedback Export to File
7110057 
Journal Article 
Short-Term Load Forecasting Using Artificial Neural Network 
Buhari, M; Adamu, SS; , 
2012 
INT ASSOC ENGINEERS-IAENG 
HONG KONG 
83-88 
WOS:000396916800017 
Artificial neural network (ANN) has been used for many years in sectors and disciplines like medical science, defence industry, robotics, electronics, economy, forecasts, etc. The learning property of ANN in solving nonlinear and complex problems called for its application to forecasting problems. This report present the development of an ANN based short-term load forecasting model for the 132/33KV sub Station, Kano, Nigeria. The recorded daily load profile with a lead time of 1-24 hours for the year 2005 was obtained from the utility company. The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back propagation algorithm for the Multilayer Feed Forward ANN model using MATLAB R2008b ANN Toolbox. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training parameters are described. The forecasted next day 24 hourly peak loads are obtained based on the stationary output of the ANN with a performance Mean Squared Error (MSE) of 5.84e(-6) and compares favorably with the actual Power utility data. The results have shown that the proposed technique is robust in forecasting future load demands for the daily operational planning of power system distribution sub-stations in Nigeria. 
Ao, SI; Castillo, O; Douglas, C; Feng, DD; Lee, JA; 
978-988-19251-1-4 
International MultiConference of Engineers and Computer Scientists (IMECS 2012) 
Hong Kong, PEOPLES R CHINA