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

Samuel, I. A. and Ekundayo, Segun and Awelewa, A. A. and Somefun, Tobilola Emmanuel and Adewale, Adeyinka A. (2020) Artificial Neural Network Base Short-term Electricity Load Forecasting: A Case Study of a 132/33 kv Transmission Sub-station. International Journal of Energy Economics and Policy, 10 (2). pp. 200-205. ISSN 2146-4553

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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 ANN 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 of 0.104 and mean squared error of 0.27.

Item Type:	Article
Uncontrolled Keywords:	Load Forecast, Transmission Substation, Artificial Neural Network, Power System
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Patricia Nwokealisi
Date Deposited:	12 Oct 2020 11:35
Last Modified:	12 Oct 2020 11:35
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/13651

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