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Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS)


Article Title: Decomposition of Electricity Data from a Nigerian Power Generating Station
by Popoola, E.O, Igboanugo, A.C, Ovuworie, G.C, and Omotehinse, S.A

The need for energy planners to carry out accurate forecast of electricity generation is imperative in the realization of overall enterprise objectives. This paper presents an exquisite decomposition model which adequately describes the original data by employing a statistical decomposition of a set of 132 month (1996-2006) electricity generation data from Ughelli Power Station, Delta State, Nigeria. The multiplicative version of the decomposition model was employed to characterize the data into its various components such as Noise, Trends, Cyclical Component, and Seasonal Index. Our results show a linear increasing trend, heavy noise, cyclical activities and seasonality. The application of statistical forecasting technique which takes into consideration the various components that are buried in the set of generation data will however, serve as a good instrument that can be adjudged intriguing and intuitively appealing for energy planners in order to come up with better maintenance and operation policies that can cub the up and down nature of the present operation and maintenance (O & M).
Keywords: decomposition, trend, seasonality, noise, cyclical component
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ISSN: 2141-7016

Editor in Chief.

Prof. Gui Yun Tian
Professor of Sensor Technologies
School of Electrical, Electronic and Computer Engineering
University of Newcastle
United Kingdom



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