Published: Jan. 10, 2025
The integration of machine learning (ML) in renewable energy systems has emerged as a pivotal strategy for enhancing efficiency, forecasting demand, and improving the stability power grids. This study presents comparative analysis adoption application ML between United States sub-Saharan Africa (SSA). made significant advancements utilizing technologies, leveraging them optimizing grid operations, consumption forecasting, waste management. Conversely, Africa, despite its vast potential, faces substantial barriers such inadequate infrastructure, limited data availability, insufficient technological capacity, hindering widespread energy. Through critical review existing literature, this identifies technological, economic, policy-related challenges that both regions face integrating into systems. While benefits from strong infrastructure investment research development, SSA is still early stages adopting ML, with considerable room growth. findings suggest while USA been successful applying to improve efficiency integrate resources, Africa’s by structural constraints, lack skilled personnel, financial challenges. paper offers policy recommendations African countries foster greater energy, including investing educational cross-border collaborations. Additionally, can play key role supporting nations through technology transfer, joint ventures, strategic investments overcome sector. In conclusion, transformative opportunity regions. Addressing infrastructural States, will be crucial achieving sustainable efficient global underscores importance international cooperation tailored frameworks advancing applications developed developing
Language: Английский