“Application of AIML and IOT for Reliable Microgrid Installed at Billenahoshalli and Lakshmanapura by Nie-Crest”

Decentralized energy solutions, particularly microgrids, have proven transformative in ensuring reliable, equitable, and sustainable access to energy in remote regions of India. This paper presents the integration of Artificial Intelligence (AI) and Machine Learning (ML) in a microgrid project implemented in the Billenahosalli and Lakshmanapura Tribal Hamlets, Mysore District, India, led by NIE-CREST. The AI-ML-driven system enhances the microgrid's decision-making processes, optimizing energy generation, consumption, and resource allocation in real-time, even under adverse weather conditions. Key AI-ML techniques such as ARIMA and LSTM models have been used to forecast weather conditions, enabling proactive energy management and resource switching between solar and biodiesel-based systems. By implementing reinforcement learning (RL), the system autonomously switches between energy sources based on predicted weather patterns, ensuring continuous electricity supply. IoT components integrated into the system provide real-time data on energy production and consumption, allowing for data-driven decisions and efficient energy use. Results demonstrate that the AI-ML-empowered microgrid consistently meets the hamlets' energy demands, generating 11,680 units of electricity annually. This energy production replaces an equivalent amount of energy from coal, resulting in a reduction of 11,680 kg of CO2 emissions and saving 46 tons of coal per year. The inclusion of energy storage, through hydrogen generation via electrolysis, ensures that surplus energy is effectively utilized. By incorporating advanced AI protocols, the microgrid improves the quality of life for residents by providing a sustainable, reliable energy supply, particularly during peak energy consumption periods and festivals.