Simulation and Machine Learning Models for Energy Policy Design

Buch | Softcover

329 Seiten

2025
Elsevier - Health Sciences Division (Verlag)
978-0-443-33971-4 (ISBN)

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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.

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.

1. Introduction: Rethinking Energy Policy in the Digital Age
2. Ethical and Regulatory Dimensions of Energy Policy Models
3. Learning from Experience: Case Studies and Best Practices
4. Foundations of Simulation and Machine Learning Techniques
5. Data-Driven Decision Making: Harnessing Energy Data for Policy
6. Simulating Energy Systems: Case Studies and Applications
7. Machine Learning Algorithms for Policy Optimization
8. Renewable Energy Integration: Challenges and Solutions
9. Efficiency Policies and Beyond: Leveraging Machine Learning
10. Adaptive Policies in Dynamic Markets: A Machine Learning Approach
11. Anticipating the Future: Trends and Emerging Technologies
12. Conclusion: Shaping the Future of Energy Policy

Erscheinungsdatum	01.11.2025
Verlagsort	Philadelphia
Sprache	englisch
Maße	152 x 229 mm
Gewicht	450 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Naturwissenschaften ► Biologie ► Ökologie / Naturschutz
	Technik ► Elektrotechnik / Energietechnik
ISBN-10	0-443-33971-6 / 0443339716
ISBN-13	978-0-443-33971-4 / 9780443339714
Zustand	Neuware