Recurrent Neural Network Model for Forecasting Electricity Demand in Nigeria

  • K. A. Abdusalam Department of Electrical and Electronics Engineering, University of Lagos, Nigeria
  • O. Adegbenro Department of Electrical and Electronics Engineering, University of Lagos, Nigeria
  • T. O. Akinbulire Department of Electrical and Electronics Engineering, University of Lagos, Nigeria
  • T. O. Akinbulire Department of Electrical and Electronics Engineering, University of Lagos, Nigeria
Keywords: energy, Levenberg-Marquardt, modular network, neurons, power

Abstract

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 with an expected demand increase of 7.5 % every five year.

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How to Cite
Abdusalam, K. A., Adegbenro, O., Akinbulire, T. O., & Akinbulire, T. O. (1). Recurrent Neural Network Model for Forecasting Electricity Demand in Nigeria. Journal of Engineering Research, 21(2), 28-38. Retrieved from http://jer.unilag.edu.ng/article/view/288