AI-driven Predictive Analytics for Renewable Energy Adoption: Socio-economic Implications for the USA

Аннотация

This study highlights the role of AI-driven predictive analytics as an emerging technology for developing renewable energy in the United States with socio-economic effects. In the present transition of America toward renewable energy resources, predictive analytics proves indispensable regarding forecasted demands of energy, optimization in resource management, and review of performance using policy intervention methods. Analyses of demographic data, energy consumption patterns, and socioeconomic indicators will enable predictive models to determine those areas with the highest potential for renewable energy deployment, but simultaneously support efforts to bridge disparities in access from one socio-economic group to another. Yet, critical challenges around data privacy, model accuracy, and targeted equity-focused policies persist. It calls for collaboration and strategic investments in the development of AI-driven predictive analytics aimed at a just and sustainable energy future for all communities across the USA. Key Words: Renewable Energy; Predictive Analytics; Socio-Economic Impact; Policy Intervention; Energy Transition