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Short-Term Load Forecasting Using The Time Series And Artificial Neural Network Methods

Samuel, I. A. and Ojewola, Tolulope and Awelewa, A. A. and Amaize, Peter (2016) Short-Term Load Forecasting Using The Time Series And Artificial Neural Network Methods. Journal of Electrical and Electronics Engineering, 11 (1). pp. 72-81. ISSN 2278-1676,p-ISSN: 2320-3331,

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Abstract

Forecasting of electrical load is very crucial to the effective and efficient operation of any power system. This is achieved by obtaining the most accurate forecast which help in minimizing the risk in decision making and reducesthe costs of operating the power plant. Therefore, the comparative study of time series and artificial neural network methods for short term load forecasting is carried out in this paper using real time load data of Covenant University,withthe moving average, exponential smoothing (time series method) and the Artificial Neural Network (ANN) models. The work was done for the day-to-day operation of the soon-to-becompleted power station of the university. For each of the methods, models were developed for the load forecast. The Artificial Neural Network proved to be the best forecast method when the results are compared in terms of error measurementswith amean absolutedeviation(MAD) having 0.225, mean squared error (MSE) having 0.095 and the mean absolute percent error(MAPE)having 8.25.

Item Type: Article
Uncontrolled Keywords: Artificial neural network, Error measurement, Exponential smoothing, Load forecasting, Moving average
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 Hannah Akinwumi
Date Deposited: 09 Aug 2016 15:36
Last Modified: 09 Aug 2016 15:36
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/6786

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