Long-Term Forecasting: A MAED Application for Sierra Leone’s Electricity Demand (2023–2050)

Neve Fields (), William Collier, Fynn Kiley, David Caulker, William Blyth, Mark Howells and Ed Brown
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Neve Fields: STEER Centre, Department of Geography & Environment, Loughborough University, Loughborough LE11 3TU, UK
William Collier: STEER Centre, Department of Geography & Environment, Loughborough University, Loughborough LE11 3TU, UK
Fynn Kiley: STEER Centre, Department of Geography & Environment, Loughborough University, Loughborough LE11 3TU, UK
David Caulker: Ministry of Energy, Government of Sierra Leone, Electricity House, 36 Siaka Steven Street, Freetown, Sierra Leone
William Blyth: Foreign, Commonwealth and Development Office, London SW1A 2AH, UK
Mark Howells: STEER Centre, Department of Geography & Environment, Loughborough University, Loughborough LE11 3TU, UK
Ed Brown: STEER Centre, Department of Geography & Environment, Loughborough University, Loughborough LE11 3TU, UK

Energies, 2024, vol. 17, issue 12, 1-17

Abstract: Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone’s electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines.

Keywords: demand forecasting; MAED; energy modelling; energy planning; capacity building; electricity; power sector (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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