Artificial Neural Network Base Short-Term Electricity Load Forecasting: A Case Study of a 132/33kv Transmission Sub-Station

Isaac Adekunle Samuel, Segun Ekundayo, Ayokunle Awelewa, Tobiloba Emmanuel Somefun and Adeyinka Adewale
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Isaac Adekunle Samuel: Electrical and Information Engineering, Covenant University, Nigeria,
Segun Ekundayo: Electrical and Information Engineering, Covenant University, Nigeria,
Ayokunle Awelewa: Department of Electrical Engineering, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria, South Africa.
Tobiloba Emmanuel Somefun: Electrical and Information Engineering, Covenant University, Nigeria,
Adeyinka Adewale: Electrical and Information Engineering, Covenant University, Nigeria,

International Journal of Energy Economics and Policy, 2020, vol. 10, issue 2, 200-205

Abstract: Forecasting of electrical load is extremely important for the effective and efficient operation of any power system. Good forecasts results help in minimizing the risk in decision making and reduces the costs of operating the power plant. This work focuses on the short-term load forecast of the 132/33KV transmission sub-station at Port-Harcourt, Nigeria, using the Artificial Neural Network (ANN). It provides accurate week-ahead load forecast using hourly load data of previous weeks. ANN has three sections namely; input, processing and output sections. There are four input parameters for the input section which are historical hourly load data (in MW), time of the day (in hours), days of the week and weekend while the output parameter after the processing (i.e. training, validation and test) is the next week hourly load predicted for the entire system. The technique used is the artificial neural network with the aid of MATLAB software. It was proven to be a good forecast method as it resulted in R-value of 0.988 which gives a mean absolute deviation (MAD) of 0.104 and mean squared error (MSE) of 0.27.

Keywords: Load forecast; transmission substation; artificial neural network; power system (search for similar items in EconPapers)
JEL-codes: C63 L94 L98 Q48 (search for similar items in EconPapers)
Date: 2020
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