International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
www.irjet.net
AI-Driven Optimization of Renewable Energy Systems: Toward Decentralized, Decarbonized, and Data-Intelligent Futures Er Ankush Pathania1*, Er. Aman Choudhary2 1,2Assistant Professor, Baddi University of Emerging Sciences & Technology Makhnumajra, Baddi, Distt. Solan, H.P.-
173205, India. --------------------------------------------------------------------------***----------------------------------------------------------------------ABSTRACT With population growth, urbanization and digitalization accelerating global energy demand, sustainability and environmental challenges continue to grow particularly because fossil fuels are still relied upon. Solar, wind, biomass, hydro and geothermal energy, collectively known as renewable energy (RE) technologies have taken positions as necessary substitutes to decarbonize the energy sector. But barriers such as intermittency, high capital costs, limited grid integration and socio political constraints exist for its deployment. In this paper we explore how Artificial Intelligence (AI) can transform the hurdles to RE, across the RE value chain, from generation and distribution to storage, consumption and governance. By improving the predictive modeling, demand forecasting, real-time optimization and lifecycle assessment, AI improves on efficiency. In addition, AI enabled tools for policy simulation, participatory planning and sustainability monitoring are indispensable for grappling with environmental trade‑offs and questions of social equity. Through synthesis of current literature, this study synthesizes recent literature to show that the integration of AI is not only about a technological upgrade, but a system wide innovation that needs to be integrated with context sensitive policy, digital infrastructure and inclusive governance. It is concluded that AI and RE technologies together create a great opportunity to enable sustainable, resilient and equitable energy transitions around the world. Keywords: Renewable Energy, Artificial Intelligence, Sustainability, Energy Transition, Decarbonization, Smart Grids, Biomethane, Lifecycle Assessment, Energy Equity, Predictive Analytics.
1. Introduction This draws demand for unprecedented global energy needs during times of expanding global economy, rapid urbanization, digitalization and rapidly expanding global population during the 21st century. The International Energy Agency (IEA) in its latest (2016) report shows that if no transformative policy and technology (smart) shifts are made to shift from fossil fuels, then global energy consumption will grow by almost 50 percent by 2050, in which fossil fuels will remain significant contributor in terms of energy supply. However this alarming trajectory is environmentally problematic, significantly because of the accelerated global climate change owing to anthropogenic greenhouse gas (GHG) emissions [1]. It is explicitly warned by the Intergovernmental Panel on Climate Change (IPCC) that for avoiding catastrophic climate tipping points, global warming needs to be limited to 1.5°C above the preindustrial levels. Such a transition requires a massive shift to renewable from fossil based energy systems. Tsolakis et al. [6] also suggest the dual implementation of both AI and blockchain technology for sustainable and transparent renewable energy supply chains. Through their work they draw out how AI can be leveraged to perform predictive maintenance, demand forecasting and operational optimization in the decentralized energy networks and how blockchain ensures traceability, trust and data integrity amongst the various stakeholders. In particular, this duality of smart technologies is highly applicable to renewable systems, one where efficiency and resilience are very important under distributed architectures. Table 1: Summary of Barriers and Opportunities in Renewable Energy Systems Category
Key Barriers
AI/Tech Opportunities
Technical
Intermittency, grid integration, energy storage
Smart grids, AI-based load balancing, predictive control
Economic
High capital cost, volatile incentives
AI-enhanced pricing, blockchain contracts, market forecasting
Operational
Maintenance complexity
© 2025, IRJET
|
and
diagnostics,
real-time
Impact Factor value: 8.315
|
control
Predictive maintenance, anomaly detection, digital twins
ISO 9001:2008 Certified Journal
|
Page 200