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TitleDeveloping a neural network model to predict the electrical load demand in the Mangaung municipal area
AuthorNigrini, Lucas Bernardo
SubjectCentral University of Technology, Free State - Dissertations
SubjectNeural networks (Computer science)
SubjectElectric power consumption - Forecasting - South Africa
SubjectElectric power plants - Load - Forecasting
SubjectTime-series analysis - Computer programs
SubjectDissertations, academic - South Africa - Bloemfontein
Date2014-10-18T21:23:51Z
Date2014-10-18T21:23:51Z
Date2012
TypeThesis
Format2 378 017 bytes
Formatapplication/pdf
AbstractThesis (D. Tech. (Engineering: Electric)) -- Central University of technology, 2012
AbstractBecause power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers. This project developed a model for short term load forecasting using a neural network. The concept was tested by evaluating the forecasting potential of the basic feedforward and the cascade forward neural network models. The test results showed that the cascade forward model is more efficient for this forecasting investigation. The final model is intended to be a basis for a real forecasting application. The neural model was tested using actual load data of the Bloemfontein reticulation network to predict its load for half an hour in advance. The cascade forward network demonstrates a mean absolute percentage error of less than 5% when tested using four years of utility data. In addition to reporting the summary statistics of the mean absolute percentage error, an alternate method using correlation coefficients for presenting load forecasting performance results are shown. This research proposes that a 6:1:1 cascade forward neural network can be trained with data from a month of a year and forecast the load for the same month of the following year. This research presents a new time series modeling for short term load forecasting, which can model the forecast of the half-hourly loads of weekdays, as well as of weekends and public holidays. Obtained results from extensive testing on the Bloemfontein power system network confirm the validity of the developed forecasting approach. This model can be implemented for on-line testing application to adopt a final view of its usefulness.
Publisher[Bloemfontein?] : Central University of Technology, Free State
Identifierhttp://hdl.handle.net/11462/176