Strategic forecasting of electricity demand for 100 % electrification in Malawi by 2063: A data-driven ECEEMDAN-BiGRU and quantile regression approach

Sylvester William Chisale, Han Soo Lee and Manuel Alejandro Soto Calvo

Energy, 2025, vol. 332, issue C

Abstract: Malawi faces significant challenges in transitioning to renewable energy due to a low electrification rate, rapid urbanization, and population growth, which drive increased electricity demand, particularly in urban transportation and residential sectors. This study uses the enhanced complete ensemble empirical mode decomposition with adaptive noise (ECEEMDAN) and bidirectional gated recurrent unit (BiGRU) models to forecast electricity demand in Malawi under three scenarios: business as usual (BAU), low demand, and high demand. These scenarios consider varying industrialization rates and policy effectiveness. By 2060, electricity demand is expected to range from 8219.47 MW in the low demand scenario to 9492.14 MW in the high demand scenario, with the BAU scenario estimating 8544.64 MW. To achieve full electrification by 2060, the BAU scenario predicts an annual increase of about 201.7 MW, while the low and high demand scenarios suggest yearly increases of 193.31 MW and 224.69 MW, respectively. These findings underscore the urgent need for strategic energy planning, infrastructure enhancement, and sustainable economic growth. The study provides valuable insights to guide policymakers and stakeholders in supporting Malawi's Agenda 2063 through effective energy development and planning.

Keywords: Malawi'S agenda 2063; Electricity demand forecasting; Renewable energy transition; ECEEMDAN; Quantile regression; Sustainable energy planning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:332:y:2025:i:c:s0360544225028543

DOI: 10.1016/j.energy.2025.137212

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