Spatiotemporal Assessment of Socio-Technical Factors in Deploying AI-Based Renewable Energy Solutions in Agricultural Communities

In terms of energy, the potential for integrating artificial intelligence (AI) and renewable energy (RE) systems is significant. In the agricultural communities where people live energy poor, rural lifestyles and where 80% of the world's energy poor live, opportunities to integrate AI-enabled RE systems that include solar irrigation, smart energy storage and predictive energy management will support maximizing electricity generation, storage and use up to potentially 30-50% improvement in energy efficiency and reduce energy costs for farmers by 40% or more. Though we have made great strides in terms of AI-enabled RE systems, thus far the deployment of such systems has been inconsistent, with non-technical barriers being the most prominent obstacle to success. The socio-technical barriers include, community resistance, disenfranchised levels of digital literacy, limited digital infrastructure, and greater capacity for sound governance. only less than 20% of rural households have digital tools to capably manage smart energy systems conditions. This review offers a socio-technical framework that embeds AI-enabled renewable energy systems into the lived experience of social behaviors, institutional environments, and infrastructural realities of rural farming communities. Article identifies five key components; participatory technology design, localized AI training, digital infrastructure development, cooperative governance, and ethical AI. Building on multiple case studies in Bangladesh, India, Kenya, and Ghana, the review found initiatives with up to 90% repayment rates, improvements in energy downtime of 25-40%, and increasing energy and water-use efficiencies of 30-45%. This socio-technical framework offers a scalable, inclusive pathway of digitally-enabled, climate-resilient agricultural energy systems.