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International Journal of Electrical and Electronic Science

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Artificial Neural Network for Energy Demand Forecast

International Journal of Electrical and Electronic Science
Vol.5 , No. 1, Publication Date: Jan. 25, 2018, Page: 8-13

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Authors

[1]	Akpama Eko James, Department of Electrical and Electronics Engineering, Cross River University of Technology, Calabar, Nigeria.
[2]	Vincent Nsed Ogar, Department of Electrical and Electronics Engineering, Cross River University of Technology, Calabar, Nigeria.
[3]	Iwueze Ifeanyi Moses, Department of Electrical and Electronics Engineering, Cross River University of Technology, Calabar, Nigeria.

Abstract

The importance of forecasting of electricity demand cannot be over emphasized. Load forecasting is needed by utility companies for planning, scheduling, cost management, equipment installation etc. Hence when utility managers do not have accurate estimate of future needs, it becomes difficult to plan. This paper is a study of electric power forecasting in Owerri city (South East Nigeria), using artificial neural network model. MATLAB tool is used in simulating this model. The model, a multilayer time delayed feed-forward artificial neural network trained with error back propagation algorithm, is used to study the pre-historical load pattern of Owerri city power system network in a supervised training manner. After presenting the model with a reasonable number of training samples, which is the historic load demand between 2007 and 2017, the model could forecast correctly the average annual electric power demand in Owerri city for the next ten years (2018 to 2027) as 111.6MW. This means that the present installed capacity will not be able to adequately serve Owerri city in about ten years’ time except an expansion in the generation capacity is done annually by about 7%.

Keywords

Load Forecasting, Electricity Demand, Artificial Neural Network

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