Simulation and Machine Learning Models for Energy Policy Design explores how policy design can reduce emissions in support of climate action by emphasizing the integration of cutting-edge simulation and machine learning techniques and bridging the gap between theoretical frameworks and practical implementation, therefore offering a hands-on guide for policymakers and professionals seeking innovative solutions. This book not only explores machine learning but also incorporates simulation techniques, providing a more comprehensive guide that extends beyond efficiency to encompass the entire policy design process.

It not only addresses renewable (and other forms of) energy integration challenges but also leverages advanced technologies for optimized decision-making. With its holistic approach and insights on practical implementation, this book is a welcome reference for those who work on the design of energy policies.

• Addresses energy policy’s role in climate change that are inline with the growing demand for renewable energy sources and the increasing complexity of energy systems
• Discusses the application of technology as applied to policy design
• Contributes to the ongoing dialogue on shaping a future where energy policies are dynamic, data-driven, and adept at fostering a sustainable energy ecosystem