Simulation and Machine Learning Models for Energy Policy Design
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

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Simulation and Machine Learning Models for Energy Policy Design
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

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Simulation and Machine Learning Models for Energy Policy Design

Simulation and Machine Learning Models for Energy Policy Design

by MSc Adedoyin BSc (Editor)
Simulation and Machine Learning Models for Energy Policy Design

Simulation and Machine Learning Models for Energy Policy Design

by MSc Adedoyin BSc (Editor)

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Overview

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


Product Details

ISBN-13: 9780443339721
Publisher: Elsevier Science
Publication date: 11/01/2025
Sold by: Barnes & Noble
Format: eBook
Pages: 329
File size: 24 MB
Note: This product may take a few minutes to download.

About the Author

Festus is a Fellow of the Higher Education Academy, a Senior Lecturer and Programme Leader for BSc Business Computing with Analytics, Data Science and Artificial Intelligence at the Department of Computing and Informatics, Bournemouth University, U.K. His current research interest is in applying Artificial Intelligence, Machine and Deep Learning, and Econometrics tools to research stories in Energy and Tourism Economics and Finance and Digital Health. Festus has contributed to several thematic areas in the UN's Sustainable Development Goals and is open to international research collaborations.

Table of Contents

1. Introduction: Rethinking Energy Policy in the Digital Age2. Ethical and Regulatory Dimensions of Energy Policy Models3. Learning from Experience: Case Studies and Best Practices4. Foundations of Simulation and Machine Learning Techniques5. Data-Driven Decision Making: Harnessing Energy Data for Policy6. Simulating Energy Systems: Case Studies and Applications7. Machine Learning Algorithms for Policy Optimization8. Renewable Energy Integration: Challenges and Solutions9. Efficiency Policies and Beyond: Leveraging Machine Learning10. Adaptive Policies in Dynamic Markets: A Machine Learning Approach11. Anticipating the Future: Trends and Emerging Technologies12. Conclusion: Shaping the Future of Energy Policy
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