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.
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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.
Written by a team of international experts, 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, Simulation and Machine Learning Models for Energy Policy Design will be a welcome reference for those who work on the design of energy policies.
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Paperback. Condizione: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9780443339714
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