4.6 Article

Long-term evolution of energy and electricity demand forecasting: The case of Ethiopia

Journal

ENERGY STRATEGY REVIEWS
Volume 36, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.esr.2021.100671

Keywords

Scenarios; Energy demand forecasting; LEAP; Developing country; Ethiopia

Categories

Funding

  1. Swedish International Development Cooperation Agency (SIDA)

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This study focuses on the crucial long-term energy demand forecasting for Ethiopia, using the LEAP system to explore different possible futures and forecast energy requirements. Six different scenarios are developed to show the maximum expected rise in demand under different drivers and energy saving opportunities, providing reliable references for future energy planning in Ethiopia.
Long-term energy demand forecasting is crucial for any country, in particular for developing countries with rapid developments of energy needs. This study focuses on Ethiopia, a country with a highly increasing energy demand resulting mainly from the currently low share of electricity access, rapid development of industrial parks, extensive expansion of the railway network, extensive irrigation schemes for agriculture, new cement and sugar factories, housing projects, power export plan to neighboring countries, etc. These all are on top of the 2.7% average population growth. In this study, the Long-range Energy Alternatives Planning System (LEAP) is used to explore different possible futures and also to forecast the long-term energy requirements in Ethiopia. The planning period is 33 years from 2018 to 2050. The study employs six different scenarios to unfold the future evolution. The developed scenarios are Business-As-Usual (BAU), Growth in Electrification and Urbanization (E&U), High Economic Growth (HEG) and three policy-driven, Improved Energy Efficiency (IEE-1, IEE-2 and IEE3) scenarios. The pathways represented by these scenarios can show the maximum expected rise in demand under different drivers and the best-case energy saving opportunities. The model is also used to estimate the associated greenhouse gas (GHG) emissions.

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