Digital Twins for Building Optimization: Enhancing Energy and Comfort: DE (Studies in Infrastructure and Control) - Hardcover

Wani, Mubashir Hussain; Hafiz, Faizal; Swain, Akshya Kumar

Hardcover

ISBN 10: 3032232732 ISBN 13: 9783032232731

Verlag: Springer, 2026

Inhaltsangabe

This book bridges the gap between theoretical research and practical applications in the field of smart building energy management. With a structure that offers a blend of theoretical concepts, practical implementations, and case studies, the book is designed to serve as a comprehensive resource for researchers, practitioners, and students. It navigates through control strategies, meta-heuristic approaches, intelligent controller designs, and the balancing of energy efficiency with occupant comfort, all underpinned by both academic research and industry practices. Specific pedagogical examples involving Python™ have been used within the book to aid the understanding of certain concepts, in addition to a chapter dedicated to EnergyPlus™.

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Über die Autorin bzw. den Autor

Dr. Mubashir Hussain Wani is Chartered Engineer and Energy Systems Researcher, currently working as Electrical Engineering Professional in the building services engineering sector at Jacobs Engineering Inc., UAE. He earned his Ph.D. in electrical and computer engineering from the University of Auckland, where he received the prestigious MBIE R&D Fellowship (via Callaghan Innovation) for his research on intelligent control strategies for building energy management systems (BEMS). His work integrates digital twins, machine learning, and multi-objective optimization to enhance energy efficiency and occupant comfort in smart buildings. His core interests include control systems, application of computational intelligence in buildings, and smart grids. Dr. Wani also holds an M.Sc. in energy and sustainability from the University of Southampton, United Kingdom, and a B.Eng. (Hons) in electronics and communication engineering. He has published in high-impact journals and IEEE conferences and is passionate about bridging research and real-world engineering to advance sustainable infrastructure.

Faizal Hafiz received the Ph.D. degree in electrical engineering from the University of Auckland, Auckland, New Zealand, in 2020. He is currently Postdoctoral Research Fellow with the Artificial Intelligence Institute, SKEMA Business School, Université Côte d’Azur, Sophia Antipolis, France. He was New Zealand International Doctoral Scholar of Computational Intelligence and Control from 2016 to 2020. Over his career, he held the positions of Research Assistant Professor with King Saud University, Riyadh, Saudi Arabia, from 2010 to 2016, and Utility Assistant Manager with Reliance Energy Ltd., Mumbai, India, from 2008 to 2010. He has published over 40 research articles in leading journals and conferences. His research interests include computational intelligence, machine learning, multiobjective optimization, and their applications in systems and control, healthcare, and business analytics.

Akshya Swain (Senior Member, IEEE) received the B.Sc. degree in electrical engineering, the M.E. degree in electronics and communication, and the Ph.D. degree in control engineering from the University of Sheffield, Sheffield, U.K., in 1985, 1988, and 1996, respectively. He has authored over 200 papers in international journals and conferences. His research interests include nonlinear system identification and control, machine learning, and big data. Dr. Swain is Associate Editor of IEEE SENSORS JOURNAL and Member of the editorial board of International Journal of Automation and Control and International Journal of Sensors, Wireless Communications and Control.

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This book bridges the gap between theoretical research and practical applications in the field of smart building energy management. With a structure that offers a blend of theoretical concepts, practical implementations, and case studies, the book is designed to serve as a comprehensive resource for researchers, practitioners, and students. It navigates through control strategies, meta-heuristic approaches, intelligent controller designs, and the balancing of energy efficiency with occupant comfort, all underpinned by both academic research and industry practices. Specific pedagogical examples involving Python™ have been used within the book to aid the understanding of certain concepts, in addition to a chapter dedicated to EnergyPlus™.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 3032232732
• ISBN 13: 9783032232731
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 362
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3,69115 Heidelberg, Germany; Heidelberg; 69115; Deutschland