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

被引:4
作者
Fields, Neve [1 ]
Collier, William [1 ]
Kiley, Fynn [1 ]
Caulker, David [2 ]
Blyth, William [3 ]
Howells, Mark [1 ,4 ]
Brown, Ed [1 ]
机构
[1] Loughborough Univ, STEER Ctr, Dept Geog & Environm, Loughborough LE11 3TU, England
[2] Govt Sierra Leone, Minist Energy, Elect House,36 Siaka Steven St, Freetown, Sierra Leone
[3] Foreign Commonwealth & Dev Off, London SW1A 2AH, England
[4] Imperial Coll London, Ctr Environm Policy, London SW7 2AZ, England
关键词
demand forecasting; MAED; energy modelling; energy planning; capacity building; electricity; power sector; SUSTAINABLE DEVELOPMENT; ENERGY DEMAND;
D O I
10.3390/en17122878
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
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页数:17
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