Systematic literature review of load profiling studies in Africa: bridging the knowledge gap on energy consumption in developing regions
Abstract
Understanding and optimizing energy consumption in less-developed regions is crucial for addressing energy poverty and driving sustainable development. This study provides a novel systematic literature review (SLR) of load profile modelling and analysis specifically targeting developing and less-developed regions in Africa. Unlike previous reviews, this study (I) identifies the most reliable methodologies for load profiling, (II) evaluate techniques for improving consumption estimation, and (III) highlight unresolved gaps in the literature. Key innovations of this review include a comprehensive qualitative and quantitative comparison of methodological techniques, clear categorization of trends, and explicit identification of the most accurate forecasting methods. The findings indicate that, although regression models are widely utilized, machine learning techniques consistently demonstrate superior accuracy. Crucially, this review also highlights significant challenges such as data scarcity, heterogeneity in methodologies, and limited availability of standardized data for comparative analysis. These findings offer a synthesized foundation for future research, providing practical guidance for researchers and policymakers seeking to enhance load forecasting accuracy and inform electrification planning in these contexts.
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