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

Authors

  • Isaac Adekunle Samuel Covenant University
  • Segun Ekundayo Covenant University
  • Ayokunle Awelewa Tshwane University of Technology, Pretoria, South Africa
  • Tobiloba Emmanuel Somefun Covenant University http://orcid.org/0000-0003-1470-5725
  • Adeyinka Adewale Covenant University

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

JEL Classifications: C63, L94, L98, Q48

DOI: https://doi.org/10.32479/ijeep.8629

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Author Biographies

Isaac Adekunle Samuel, Covenant University

Electrical and Information Engineering Senior lecturer

Segun Ekundayo, Covenant University

Electrical and Information Engineering Graduate student 

Ayokunle Awelewa, Tshwane University of Technology, Pretoria, South Africa

Electrical Engineering Department, Faculty of Engineering and the Built EnvironmentSenior lecturer 

Tobiloba Emmanuel Somefun, Covenant University

Electrical and Information EngineeringPhD Student

Adeyinka Adewale, Covenant University

Electrical and Information Engineering Senior lecturer

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Published

2020-01-21

How to Cite

Samuel, I. A., Ekundayo, S., Awelewa, A., Somefun, T. E., & Adewale, A. (2020). Artificial Neural Network Base Short-Term Electricity Load Forecasting: A Case Study of a 132/33kv Transmission Sub-Station. International Journal of Energy Economics and Policy, 10(2), 200–205. Retrieved from https://www.econjournals.com/index.php/ijeep/article/view/8629

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