International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Research Paper | Electrical Engineering | Ethiopia | Volume 5 Issue 2, February 2016

Long Term Load Forecasting of Jimma Town for Sustainable Energy Supply

Emiyamrew Minaye [2] | Melaku Matewose

Abstract: Now a day, due to economic development of the country the electrical energy demand of Ethiopia increases by 30 % yearly. But in order to satisfy customers power demand the planning and expansion of power must be done using a proper load forecasting methods. Due to this demand forecasting is a vital and integral process for planning periodical operations and facility expansion in the electricity sector. Even though demand forecasting is a central process its pattern is almost very complex due to the deregulation of energy markets. Therefore, finding an appropriate forecasting model that will generalize the demand patter for a specific electricity network may not be an easy task. This paper presents a realistic methodology that can be used as a guide to construct Jimma town Electric Power Load Forecasting models. Trending methodology statistical analyses are involved to study the load features and forecasting precision, such as linear regression, compound growth model and quadratic regression. Real monthly and yearly load data from Jimma distribution system substation is used as a case study. By using the best optimal value of the rank correlation coefficient and mean absolute percentage error, the compound growth model is used in the coming five years load forecasting. By forecasting of Jimma town load demand will result in proper utilization of energy and for planning of any electricity related projects it will use as a baseline to be applied cost wise. The main objective of this study is to assess the future energy demand of customers so that supplying of power and using that without shortage will be optimized.

Keywords: Energy, Forecasting, Long term, Trend analysis, Customers

Edition: Volume 5 Issue 2, February 2016,

Pages: 1500 - 1504

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