Towards Sustainable AI-Driven Renewable Energy Systems through Integration of Forecasting, Grid Economics and Lifecycle Assessment

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🔬研究者:The unified framework and identified research gaps offer a comprehensive research agenda for AI in renewable energy systems.

🏢実務担当者:Operational cost savings of 18.7% and improved forecasting accuracy demonstrate clear business value for utility and energy companies.

🏛政策担当者:The policy-aware optimization and recognition of equity challenges provide insights for designing supportive regulatory frameworks.

The rapid deployment of renewable energy systems has intensified the need for intelligent, scalable, and economically viable solutions to manage variability, uncertainty, and grid complexity. Artificial intelligence (AI) has emerged as a transformative enabler, significantly improving forecasting accuracy, operational efficiency, and system resilience. However, existing studies largely treat AI as an isolated technical tool, overlooking its integration with economic decision-making, lifecycle sustainability, and policy constraints. This paper addresses these critical gaps by proposing a unified analytical framework that links AI-driven renewable energy forecasting with grid economics, optimization-based dispatch, and lifecycle assessment of AI energy consumption. The framework incorporates renewable generation modeling, data-driven forecasting using deep learning architectures, and cost-aware optimization while explicitly accounting for the computational energy footprint of AI systems. A quantitative evaluation using a 24-h simulation demonstrates that AI-based forecasting reduces prediction errors by nearly 50% and lowers total operational costs by 18.7% compared to conventional approaches. Importantly, the inclusion of AI energy consumption enables a realistic assessment of net system benefits, revealing that computational overhead remains marginal relative to achieved savings. Beyond technical performance, this study systematically identifies key research gaps—including the forecasting–economics disconnect, AI energy footprint, model generalization limitations, grid heterogeneity, and policy and equity challenges—and proposes actionable solutions such as domain-adaptive AI models, green AI strategies, and policy-aware optimization frameworks. The results highlight that the true value of AI in renewable energy systems lies not only in predictive accuracy but in its integration with economic, environmental, and regulatory dimensions. This work provides a comprehensive roadmap for researchers, utilities, and policymakers to design scalable, efficient, and sustainable AI-enabled energy systems aligned with global decarbonization goals.

• semanticscholar https://doi.org/10.53941/rset.2026.100005first seen 2026-06-19 05:30:01