Corporate Environmental, Social, and Governance (ESG) compliance has become a mandatory requirement for organizations due to stricter regulations, investor expectations, and global sustainability goals. Despite growing adoption of renewable energy technologies, many corporations face challenges in effectively monitoring energy usage, carbon emissions, and ESG performance using fragmented and manual systems. This paper presents an AI-integrated renewable energy and data analytics platform designed to support corporate ESG compliance through continuous monitoring, predictive analysis, and automated reporting. The proposed platform integrates renewable energy sources, smart meters, and environmental sensors to collect real time operational data. Advanced artificial intelligence and machine learning models are applied to forecast energy demand, optimize renewable energy utilization, and estimate carbon emission trends. The analytics layer transforms raw energy data into standardized ESG indicators, enabling transparent and auditable sustainability assessment aligned with international reporting frameworks. The system also supports risk identification by detecting anomalies and potential noncompliance patterns in energy consumption and emissions. Experimental evaluation using simulated corporate energy datasets demonstrates that the proposed platform improves energy efficiency, increases renewable energy penetration, and significantly reduces the time and effort required for ESG reporting. The results highlight the effectiveness of AI driven data analytics in enabling data-based decision making for sustainability management. Overall, the proposed platform provides a scalable and intelligent solution for organizations seeking to achieve reliable ESG compliance while maintaining operational efficiency and long-term environmental responsibility.
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AI-integrated renewable energy and data analytics platform for corporate ESG compliance
Semantic Scholar · 2026
Abstract
Corporate Environmental, Social, and Governance (ESG) compliance has become a mandatory requirement for organizations due to stricter regulations, investor expectations, and global sustainability goals. Despite growing adoption of renewable energy technologies, many corporations face challenges in effectively monitoring energy usage, carbon emissions, and ESG performance using fragmented and manual systems. This paper presents an AI-integrated renewable energy and data analytics platform designed to support corporate ESG compliance through continuous monitoring, predictive analysis, and automated reporting. The proposed platform integrates renewable energy sources, smart meters, and environmental sensors to collect real time operational data. Advanced artificial intelligence and machine learning models are applied to forecast energy demand, optimize renewable energy utilization, and estimate carbon emission trends. The analytics layer transforms raw energy data into standardized ESG indicators, enabling transparent and auditable sustainability assessment aligned with international reporting frameworks. The system also supports risk identification by detecting anomalies and potential noncompliance patterns in energy consumption and emissions. Experimental evaluation using simulated corporate energy datasets demonstrates that the proposed platform improves energy efficiency, increases renewable energy penetration, and significantly reduces the time and effort required for ESG reporting. The results highlight the effectiveness of AI driven data analytics in enabling data-based decision making for sustainability management. Overall, the proposed platform provides a scalable and intelligent solution for organizations seeking to achieve reliable ESG compliance while maintaining operational efficiency and long-term environmental responsibility.