Intelligent Data Analytics for Power and Energy Systems (eBook)
XXII, 641 Seiten
Springer Singapore (Verlag)
978-981-16-6081-8 (ISBN)
This book brings together state-of-the-art advances in intelligent data analytics as driver of the future evolution of PaE systems. In the modern power and energy (PaE) domain, the increasing penetration of renewable energy sources (RES) and the consequent empowerment of consumers as a central and active solution to deal with the generation and development variability are driving the PaE system towards a historic paradigm shift. The small-scale, diversity, and especially the number of new players involved in the PaE system potentiate a significant growth of generated data. Moreover, advances in communication (between IoT devices and M2M: machine to machine, man to machine, etc.) and digitalization hugely increased the volume of data that results from PaE components, installations, and systems operation. This data is becoming more and more important for PaE systems operation, maintenance, planning, and scheduling with relevant impact on all involved entities, from producers, consumer,s and aggregators to market and system operators. However, although the PaE community is fully aware of the intrinsic value of those data, the methods to deal with it still necessitate substantial enhancements, development and research. Intelligent data analytics is thereby playing a fundamental role in this domain, by enabling stakeholders to expand their decision-making method and achieve the awareness on the PaE environment. The editors also included demonstrated codes for presented problems for better understanding for beginners.
Dr. Hasmat Malik (Senior Member IEEE) received M.Tech degree in electrical engineering from the National Institute of Technology (NIT) Hamirpur, Himachal Pradesh, India, and the Ph.D. degree in Electrical Engineering from Indian Institute of Technology (IIT), Delhi. He has served as an assistant professor for more than five years at the Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT), Dwarka, Delhi, India. He is currently a chartered engineer (CEng) and a professional engineer (PEng). He is also a research fellow with Berkeley Education Alliance for Research in Singapore (BEARS), a research centre of the University of California, Berkeley, University Town, National University of Singapore (NUS), Singapore, since January, 2019. He has published widely in international journals and conferences his research findings related to intelligent data analytics, artificial intelligence, and machine learning applications in power system, power apparatus, smart building and automation, smart grid, forecasting, prediction and renewable energy sources. Dr. Hasmat has authored/co-authored more than 100 research papers and eight books and thirteen chapters in nine other books, published by IEEE, Springer, and Elsevier. He has supervised 23 PG students. His principal area of research interests is artificial intelligence, machine learning and big data analytics for renewable energy, smart building and automation, condition monitoring and online fault detection and diagnosis (FDD). Dr. Malik is also a member of the Computer Science Teachers Association (CSTA), the Association for Computing Machinery (ACM) EIG, the Institution of Engineering and Technology (IET), UK, and Mir Labs, Asia, a life member of the Indian Society for Technical Education (ISTE), the Institution of Engineers (IEI), India, and the International Society for Research and Development (ISRD), London, and a fellow of the Institution of Electronics and Telecommunication Engineering (IETE). He received the POSOCO Power System Award (PPSA-2017) for his Ph.D. work for research and innovation in the area of power systems. He also received the Best Research Papers Awards from IEEE INDICON-2015 and the Full Registration Fee Award from IEEE SSD-2012, Germany.
Dr. Md. Waseem Ahmad (Member, IEEE) received the B.Tech. and M.Tech. degrees in electrical engineering from Aligarh Muslim University, Aligarh, India, in 2008 and 2011, respectively, and the Ph.D. degree in electrical engineering from the Indian Institute of Technology Kanpur, Kanpur, India, in 2018. He worked as a research fellow with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore, and a graduate trainee engineer with Siemens Ltd., India. He is currently an assistant professor with the National Institute of Technology Karnataka, Surathkal, India. His research interests include fault diagnostics and condition monitoring of power electronic converters.
This book brings together state-of-the-art advances in intelligent data analytics as driver of the future evolution of PaE systems. In the modern power and energy (PaE) domain, the increasing penetration of renewable energy sources (RES) and the consequent empowerment of consumers as a central and active solution to deal with the generation and development variability are driving the PaE system towards a historic paradigm shift. The small-scale, diversity, and especially the number of new players involved in the PaE system potentiate a significant growth of generated data. Moreover, advances in communication (between IoT devices and M2M: machine to machine, man to machine, etc.) and digitalization hugely increased the volume of data that results from PaE components, installations, and systems operation. This data is becoming more and more important for PaE systems operation, maintenance, planning, and scheduling with relevant impact on all involved entities, from producers, consumer,s and aggregators to market and system operators. However, although the PaE community is fully aware of the intrinsic value of those data, the methods to deal with it still necessitate substantial enhancements, development and research. Intelligent data analytics is thereby playing a fundamental role in this domain, by enabling stakeholders to expand their decision-making method and achieve the awareness on the PaE environment. The editors also included demonstrated codes for presented problems for better understanding for beginners.
Erscheint lt. Verlag | 17.2.2022 |
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Reihe/Serie | Lecture Notes in Electrical Engineering | Lecture Notes in Electrical Engineering |
Zusatzinfo | XXII, 641 p. 412 illus., 271 illus. in color. |
Sprache | englisch |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Technik ► Elektrotechnik / Energietechnik | |
Technik ► Maschinenbau | |
Schlagworte | Condition Monitoring and Diagnostics of Devices • Data Mining • machine learning • Optimization • Prices Monitoring and Forecasting • Smart Grid • Smart Health Monitoring of Power and Energy Systems • Smart Management System • Smart Power and Energy Systems • System Health Forecast and Prediction |
ISBN-10 | 981-16-6081-6 / 9811660816 |
ISBN-13 | 978-981-16-6081-8 / 9789811660818 |
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