Artificial Intelligence for Energy Efficiency
- 1st Edition - January 1, 2026
- Imprint: Elsevier
- Authors: Dasheng Lee, Ming-Shan Jeng, I-Haur Tsai
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 6 7 2 9 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 6 7 3 0 - 4
Artificial Intelligence for Energy Efficiency is a comprehensive exploration of AI application technologies. The book integrates AI algorithms with the operational princi… Read more
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Artificial Intelligence for Energy Efficiency is a comprehensive exploration of AI application technologies. The book integrates AI algorithms with the operational principles of electrical machinery and energy systems and introduces how these algorithms can alter the operational characteristics of such systems. With a focus on their use in enhancing energy efficiency, it imparts interdisciplinary knowledge and a deep understanding of the principles and methods of AI applications. The book begins with an overview of global trends in enhancing energy efficiency, followed by an introduction to AI algorithms applied in this field. Subsequent chapters, supported by industry case studies, delve into AI applications in enhancing motor efficiency, building and energy saving for building equipment, and smart city development. The book also addresses AI computing power saving and discusses the evolution of AI and the limits of energy efficiency improvement. It concludes with future prospects and the most beneficial application of AI for sustainable human development. Artificial Intelligence for Energy Efficiency is an essential read for graduate students in mechanical, electrical, architectural, and energy-related fields, as well as researchers interested in the intersection of AI and energy efficiency. This comprehensive guide offers a wealth of research case studies, making it a valuable reference tool for professionals in the energy sector.
- Explores the integration of AI algorithms with the operational principles of electrical machinery and energy systems, offering an in-depth understanding of how AI can enhance energy efficiency
- Analyses real-world AI application case studies in factories, buildings, and cities, providing practical insights into the effectiveness of AI applications in improving energy efficiency
- Evaluates the energy consumption of AI computations, proposing solutions to perform more computations with relatively lower energy consumption
- Includes reference designs for AI-driven energy-efficient motors, HVAC systems, smart buildings, and sustainable cities
Students, researchers, and engineers working in the fields of energy, electrical, and mechanical engineering and involved in energy efficiency applications in the built environment
1. Global Trends in Enhancing Energy Efficiency Demand
2. Introduction to AI Algorithms Applied in Enhancing Energy Efficiency
3. AI Applications in Enhancing Motor Efficiency
4. AI Applications in Building & Energy Saving for Building Equipment
5, AI Applications in Smart City Development and Achieving Urban Sustainability
6. AI Computing Power Saving
7. The Evolution of AI and the Limits of Energy Efficiency Improvement
8, Conclusions and Future Prospects on Applying AI for Energy Efficiency
2. Introduction to AI Algorithms Applied in Enhancing Energy Efficiency
3. AI Applications in Enhancing Motor Efficiency
4. AI Applications in Building & Energy Saving for Building Equipment
5, AI Applications in Smart City Development and Achieving Urban Sustainability
6. AI Computing Power Saving
7. The Evolution of AI and the Limits of Energy Efficiency Improvement
8, Conclusions and Future Prospects on Applying AI for Energy Efficiency
- Edition: 1
- Published: January 1, 2026
- Imprint: Elsevier
- Language: English
DL
Dasheng Lee
Prof. Lee has published 23 SCI papers in the past five years (2019-2023), all related to the application of Artificial Intelligence (AI) in enhancing energy efficiency. These papers have been extensively cited, with an average citation count of 36 for the nine papers where he was the first author. His Field-Weighted Citation Impact (FWCI) is 1.235, and he has achieved an h-index of 20. His latest publication in "Applied Thermal Engineering" focuses on "Artificial Intelligence Enabled Energy-Efficient Heating, Ventilation, and Air Conditioning System: Design, Analysis, and Necessary Hardware Upgrades." This paper details the design methods for improving the energy efficiency of HVAC systems using AI and has already garnered 3 citations within three months of publication. These achievements demonstrate Prof. Lee's unique academic insights in AI for Energy Efficiency.
Beyond academic accomplishments, in the past five years, Prof. Lee has undertaken 46 research projects commissioned by the government and enterprises, totaling 3.1 million USD. Most of these projects relate to the application of AI in energy conservation, including in air conditioning systems, buildings, factories, and even urban public facilities. His extensive experience in industry-academia collaboration enriches his book on AI for Energy Efficiency, providing many practical examples of improved energy efficiency, making the content more comprehensive.
Affiliations and expertise
Department of Energy and Refrigerating Air-conditioning Engineering, National Taipei University of Technology, TaiwanMJ
Ming-Shan Jeng
Dr. Jeng is a Senior Principal Researcher at the Green Energy & Environment Research Laboratories in the Industrial Technology Research Institute (ITRI) in Taiwan. He earned his Ph.D. degree in 1992 from National Cheng-Kung University and has been with ITRI since 1993. In 2004, he was a visiting scholar at the Massachusetts Institute of Technology. With over 30 years of experience in energy research, his work spans building energy efficiency, LED lighting, HVAC systems, renewable energy, hydrogen energy, and thermoelectric materials. He has published over 100 articles and holds more than 30 patents in these fields. Currently, Dr. Jeng serves as the Deputy General Director of the Green Energy & Environment Research Laboratories in ITRI, where he oversees research projects in energy efficiency and clean environment.
Affiliations and expertise
Senior Principal Researcher, Green Energy & Environment Research Laboratories, Industrial Technology Research Institute (ITRI), TaiwanIT
I-Haur Tsai
Dr. Tsai received his Ph.D. in Mechanical Engineering from National Taiwan University, Taiwan, in 2021. He has been focusing on Artificial Intelligence (AI) applications in his recent research. A significant aspect of his work is integrating Reinforcement Learning with a robotic arm model, which allows the robotic arm to execute specific tasks. He presented this research at the 19th International Conference on Automation Technology. Additionally, Dr. Tsai's latest studies involve the use of deep learning for diagnosing eccentricity in permanent magnet synchronous motors and detecting faults in ball bearings.
Affiliations and expertise
Science and Technology Department of Mechanical Engineering, National Taiwan University, Taiwan