Recurrent Neural Network Model for Forecasting Electricity Demand in 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.
References
Afolabi, A. O., Olatunji, B.O. and. Ajayi, A. O (2008). Electricity Load Forecasting Using Artificial Neural Networks, Journal of Engineering and Applied Sciences, 3(2) Medwell Online Journal.
Agboola, O.P., (2011). Independent Power Producer (IPP) Participation Solution to Nigeria Power Generation problem, Proceedings of the World Congress of Engineering, III, London.
Alawode, K.O. and Oyedeji, M.O. (2013). Comparison of Neural Network models for Load Forecasting in Nigeria Power Systems, IJERT.
Alfares, H.K. and Mohammad, N. (2002). Electric Load Forecasting: Literature Survey and Classification Methods, International Journal of System Science, 33(1): 23-34.
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Bhattacharyya, S.C. and Timilsina, G.R. (2009). Energy Demand Models for Policy Formulation- A Comparative Study of Energy Demand Models, Policy Research Working Paper 4866, The World Bank Development Research Group Environment and Energy Team http://econs.worldbank.org
Dao, J. (2015). Study on Short Term Load Forecasting Method Based on the PSO and SVM Model, International Journal of Control and Automation, 8(8): 181-188.
Agboola, O.P., (2011). Independent Power Producer (IPP) Participation Solution to Nigeria Power Generation problem, Proceedings of the World Congress of Engineering, III, London.
Alawode, K.O. and Oyedeji, M.O. (2013). Comparison of Neural Network models for Load Forecasting in Nigeria Power Systems, IJERT.
Alfares, H.K. and Mohammad, N. (2002). Electric Load Forecasting: Literature Survey and Classification Methods, International Journal of System Science, 33(1): 23-34.
Baum, E. B. and Haussler, D, (1989). What Size Net Gives Valid Generalisation, Neural Computation, MIT Press, 1(1): 151-160.
Beale, Mark H., Martin, T. H. and Howard B. D, (2010). Neural Network Toolbox™ 7 User’s Guide’, Maths Works Inc.
Bhattacharyya, S.C. and Timilsina, G.R. (2009). Energy Demand Models for Policy Formulation- A Comparative Study of Energy Demand Models, Policy Research Working Paper 4866, The World Bank Development Research Group Environment and Energy Team http://econs.worldbank.org
Dao, J. (2015). Study on Short Term Load Forecasting Method Based on the PSO and SVM Model, International Journal of Control and Automation, 8(8): 181-188.
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
Section
Articles
