Short Term Load Forecasting OF 132/33Kv Maiduguri Transmission Substation Using Artificial Neural Network (ANN)
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2015, Vol 4, Issue 5
Abstract
This paper presents a novel approach for 1 to 24 hours ahead load forecasting using multilayer perceptron (MLP) also referred to as multilayer feed forward artificial neural network (ANN) of a utility company located in the North Eastern part of Nigeria. The inputs to the ANN model are; hourly load of the day, daily average minimum temperature, daily average maximum temperature, daily average minimum humidity and daily average maximum humidity. The output to the model is 24hours forecast load. The model was trained and tested on data of year 2010 using the Levenberg- marquadt optimization technique using MATLAB R2012b. A mean square error (MSE) of 5.3902e-06 was obtained. The result obtained shows that the MLP artificial neural network can be considered as a good method to model the Short term load forecast systems.
Authors and Affiliations
Idakwo Harrison, Peter Dibal, Ishaku Bello
Keywords
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