Energy poverty in rural South Africa negatively impacts the quality of education and academic performance of the learners resident there. This study presents a smart solar-powered LED system integrated with a Dynamic Energy Management System to optimize energy allocation for lighting and study time. It was tested in rural South Africa, where 16.6 million households experience energy poverty. The system leverages machine learning based on weather conditions to predict battery charge times for optimized study time and academic performance. A portable smart LED cube was introduced to ten high school students in Xigalo village, Limpopo province, South Africa, which significantly increased their study time (optimized at 9.46 hours) and improved their academic performance from an average of 52.2% to 66.6%. By harnessing solar energy for lighting and cognitive benefits, this AI-driven solution demonstrates its potential to bridge educational inequalities caused by energy poverty in South Africa and other developing countries.
Chauke et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: