The urgent need to decarbonize global energy systems has propelled renewable energy into a position of unprecedented importance, yet this shift presents major technical, economic, and policy challenges. Increasing reliance on intermittent energy sources such as solar and wind demands more effective forecasting, grid coordination, and flexibility. Artificial Intelligence (AI) offers powerful tools to meet these challenges by learning from data, modeling complex interactions, and enabling real-time optimization across generation, transmission, and consumption.

Renewable Energy Transition with Artificial Intelligence: Challenge-driven Solutions highlights successful pathways of knowledge transfer between academia and industry through case studies drawn from wind, solar, and emerging energy sources. Focusing on challenge-driven problem solving, the authors showcase transferable strategies that overcome pressing obstacles such as the lack of open datasets, the reluctance to adopt opaque predictive models, and insufficient performance benchmarks.

Contributions by leading experts emphasize explainable AI, collaborative innovation, and the vital role of shared infrastructures for data and knowledge exchange. The book also draws from the authors’ international workshop with diverse stakeholders, underscoring the importance of cross-sector cooperation in ensuring sustainable and scalable impact.

Adopting a challenge-driven framework linking AI innovation with renewable energy adoption, this title:

Integrates perspectives from academia, industry, and the public sector to identify scalable solutions
Demonstrates methods for bridging the “black box” problem in neural network–based energy forecasting
Addresses data scarcity by proposing solutions for open access, standardization, and benchmarking in renewables AI
Provides practical insights for distributed generation, storage, and demand-response management
Explores future directions for explainable AI in energy system integration and resilience

Both a roadmap and a reference point for integrating AI into renewable systems to accelerate global decarbonization, this book is designed for advanced students, researchers, and practitioners in engineering, computer science, and renewable energy. It is suitable for courses such as Renewable Energy Systems, Artificial Intelligence Applications in Engineering, and Energy Policy and Technology within graduate and postgraduate degree programs in engineering, data science, and environmental studies.

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Renewable Energy Transition with Artificial Intelligence

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