Green-AI for Eco-Intelligence with Renewable Energy Resources and Climate Actions

Abstract

With the rapid industrialization, urbanization, rising energy demand and climate change vulnerability in India, the shift to renewable energy and sustainable environmental management has picked up pace. India's efforts have been tremendous to promote Solar, Wind, Hydro and Biomass energy under various programmes like National Solar Mission, National Action Plan on Climate Change (NAPCC), and Net Zero emissions by 2070, but the challenges of intermittency, handling large-scale and heterogeneous data, computational inefficiency, and the lack of intelligent decisionsupport systems persist. Traditional approaches to energy management with AI tend to consume significant computing power and lead to higher energy usage and carbon emissions, contrary to sustainable computing practices. To overcome these issues, this paper suggests a Green Artificial Intelligence (Green AI) empowered Eco-Intelligence Framework for intelligent management of renewable energy and climate action in the context of India. The proposed system consists of a Multi-source Renewable Energy Data Module (MREDM), a meteorological module, a Satellite Imagery module, a Smart grid module, an Environment module, and an Adaptive Environmental Data Integration Module (AEDIM) for the creation of a single sustainability knowledge base. The A Renewable Energy Forecasting Module (REFM) is a light-weight Deep learning model that can precisely predict solar PV and wind power generation in different climatic regions of India in a complex computing environment. It then dynamically optimizes computational resources, model architectures and energy scheduling strategies to reduce energy consumption, execution time and carbon emissions while maintaining prediction accuracy via a Carbon-Aware Green AI Optimization Module (CGAOM). Also, an Eco-Intelligence Decision Support Module (EIDSM) analyses renewable energy use, carbon reduction potential, grid stability, energy efficiency, and climate resilience to give energy management strategies for policy makers, energy providers and smart city decision makers. The Explainable Climate Action Module (ECAM) deepens the transparency of AI by offering interpretable suggestions that assist in creating sustainable policies and building stakeholder trust. Explainable Climate Action Module (ECAM) increases transparency by delivering interpretable AI recommendations that help create sustainable policies and gain stakeholder confidence. Case studies on Indian renewable energy resources and climate data show that the GreenAI-Eco framework enhances the accuracy of renewable energy forecasting, computational efficiency, carbon emissions savings, and sustainable energy use over traditional AI-driven methods. It is a successful framework that helps in intelligent energy planning for India's varied climatic zones and also helps to minimise computational energy needs by following Green AI principles. In conclusion, the proposed system offers a scalable, energy efficient, and environmentally friendly approach to expediting India's shift towards clean energy and climate resilience.