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.
1. Introduction - p. xix 2. Chapter ONE. Introduction: Rethinking energy policy in the digital age - p. 1 3. Chapter Two. Ethical and regulatory dimensions of energy policy models - p. 19 4. Chapter Three. Data-driven decision making: Harnessing energy data for policy - p. 42 5. Chapter Four. Simulating energy systems: Case studies of food waste management and environmental degradation in the USA - p. 75 6. Chapter Five. Machine learning algorithms for policy optimization. Application to energy consumption in manufacturing - p. 108 7. Chapter Six. Efficiency policies and beyond: Leveraging machine learning - p. 129 8. Chapter SEVEN. Efficiency of simulation and machine learning algorithms for modeling and forecasting greenhouse gas emissions: A review - p. 161 9. Chapter Eight. Sustainable renewable energy transitions: Data-driven strategies for the East African Community (EAC) - p. 183 10. Chapter 9. A machine learning-based simulation experiment on the impact of transportation energy consumption on carbon emissions in the United Kingdom - p. 193 11. Chapter Ten. Plastic wastes and sustainability targets in Germany: A policy simulation experiment using machine learning - p. 209 12. Chapter Eleven. Energy use modeling and tracking in a university environment using machine learning algorithms - p. 217 13. Index - p. 293
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.
1. Introduction - p. xix 2. Chapter ONE. Introduction: Rethinking energy policy in the digital age - p. 1 3. Chapter Two. Ethical and regulatory dimensions of energy policy models - p. 19 4. Chapter Three. Data-driven decision making: Harnessing energy data for policy - p. 42 5. Chapter Four. Simulating energy systems: Case studies of food waste management and environmental degradation in the USA - p. 75 6. Chapter Five. Machine learning algorithms for policy optimization. Application to energy consumption in manufacturing - p. 108 7. Chapter Six. Efficiency policies and beyond: Leveraging machine learning - p. 129 8. Chapter SEVEN. Efficiency of simulation and machine learning algorithms for modeling and forecasting greenhouse gas emissions: A review - p. 161 9. Chapter Eight. Sustainable renewable energy transitions: Data-driven strategies for the East African Community (EAC) - p. 183 10. Chapter 9. A machine learning-based simulation experiment on the impact of transportation energy consumption on carbon emissions in the United Kingdom - p. 193 11. Chapter Ten. Plastic wastes and sustainability targets in Germany: A policy simulation experiment using machine learning - p. 209 12. Chapter Eleven. Energy use modeling and tracking in a university environment using machine learning algorithms - p. 217 13. Index - p. 293
