AI-Powered Energy Forecasting and Control for Smart Rural Energy Infrastructure

It is at the center of such a critical transition that Bangladesh is currently involved in through a rural electrification-led transition to power agriculture, health, education and livelihoods. Despite over 95% grid penetration in rural areas, the energy availability is only between 18-22% owing largely to intermittent n renewable sources, inadequate infrastructure and seasonal demand. Artificial Intelligence (AI), specifically under the disciplines of machine learning (ML) and deep learning (DL) are transforming rural energy systems by providing more accurate forecasts, enabling intelligent control and improving energy efficiency. In this cross-cutting review, we consider how AI is being integrated into rural energy systems in Bangladesh, with a specific focus on energy forecasting, smart microgrids, and adaptive load management. Artifact observations in districts such as Gaibandha, Patuakhali and Kumigram demonstrated how AI-based systems can improve forecasting accuracy to over 92% reduce system down-time to 80%, and improve battery storage by 30-35%. The paper discussed additionally, coupling AI with IoT for real-time controls, GIS with 47% more efficient renewable site optimization for solar siting, and block chain with decentralized energy transactions that reduced the average household cost of energy by 11%. The study ended with some important recommendations that detailed how AI can be an enabler for sustainable and equitable rural energy development in Bangladesh, assuming it is deployed on an inclusive and context specific platform.