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Labrune, M.P., and J.W. Taylor, 2001. Short term electricity demand using double seasonal exponential smoothing. J. Operat. Res. Soc, 54: 799-805.

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Article

Analysis of Least Square and Exponential Regression Techniques for Energy Demand Requirement (2013-2032)

1Department of Electrical/Computer Engineering, Rivers State University, Port Harcourt, Rivers State Nigeria

2Faculty of Engineering, Rivers State University, Port Harcourt, Rivers State Nigeria


American Journal of Electrical and Electronic Engineering. 2018, Vol. 6 No. 2, 38-59
DOI: 10.12691/ajeee-6-2-1
Copyright © 2018 Science and Education Publishing

Cite this paper:
S. L. Braide, E. J. Diema. Analysis of Least Square and Exponential Regression Techniques for Energy Demand Requirement (2013-2032). American Journal of Electrical and Electronic Engineering. 2018; 6(2):38-59. doi: 10.12691/ajeee-6-2-1.

Correspondence to: S.  L. Braide, Department of Electrical/Computer Engineering, Rivers State University, Port Harcourt, Rivers State Nigeria. Email: Braidesepiribo@yahoo.com

Abstract

This paper considered a long term electric power load forecast for twenty years (20 years) projection, in Nigeria power system using least-square regression and exponential regression model. The model is implemented in Matlab platform with a plot in residential load demand, commercial load demand and industrial load demand in (MW). In the quest for analysis and predicting the energy (power) demand (MW) requirement for a projection period of (2013 - 2032), data are collected between (2000 - 2012), from the Central Bank of Nigeria (CBN), and National Bureau of statistics (NBS). The results obtained shows that energy generated from the respective generating station including Egbin thermal power station Lagos, Sapele thermal power station etc. are grossly inadequate. This mismatch is a major problem in power system planning and operation. The result also shows that there is deviation between predicted energy demand (MW) and available power (or capacity allocated). The predicted energy demand into the projected future of 20years is 45 5,870.2MW. The paper work also extended the prediction form into: least-square, exponential regression model. Evidently, the comparism plot for linear and exponential model which shows similar predicting pattern: particularly least-square exhibit linear behavior, while exponential shows non-linear behaviour, the linear model gives more accurate result as compared to the exponential.

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