Recurrent Neural Network Model for Forecasting Electricity Demand in Nigeria
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Journal of Engineering Research
This work uses modular recurrent neural network to estimate the electricity demand in Nigeria from 2015 to 2050. The network is a 2-layer multi-input, single-output model with twelve neurons trained using Levenberg-Marquardt algorithm. The data structure used for training is cell array of sequential concurrent data. The Recurrent Neural Network model was simulated as Non-linear Auto Regressive with eXogenous (NARX) model in Matlab environment and the predicted load for 2015 is about 550GWh; and an expected demand increase of 7.5 % every five year.
energy, Levenberg-Marquardt, modular network, neurons, power