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Predictive Modelling for Energy Management and Power Systems Engineering
- 1st Edition - September 30, 2020
- Editors: Ravinesh Deo, Pijush Samui, Sanjiban Sekhar Roy
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 7 7 7 2 - 3
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 7 7 7 3 - 0
Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy… Read more
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Request a sales quotePredictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets.
- Presents advanced optimization techniques to improve existing energy demand system
- Provides data-analytic models and their practical relevance in proven case studies
- Explores novel developments in machine-learning and artificial intelligence applied in energy management
- Provides modeling theory in an easy-to-read format
Postgraduate researchers, early and mid-career scholars, expert academics, renewable energy practitioners, electrical and electronic engineers, climate scientists and future energy policy-makers
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NOUR EL YAKINE KOUBA - PV panels lifespan increase by control
Bechara NEHME - Community-scale rural energy systems: General planning algorithms and management methods in developing countries
A. López-González - Proven ESS Applications for Power System Stability and Transition Issues
Jean Ubertalli - Forecasting solar radiation with evolutionary polynomial regression, wavelet transform & ensemble empirical mode decomposition
Mohammad Rezaie-Balf, Sungwon Kim, Alireza Ghaemi and Ravinesh C. Deo - Development and Comparison of Data-driven Models for Wind Speed Forecasting in Australia
Ananta Neupane, Nawin Raj, Ravinesh Deo and Mumtaz Ali - Modelling Photosynthetic Active Radiation with a Hybrid Multilayer Perceptron-Firefly Optimizer Algorithm
Harshna Lata Gounder, Zaher Munder Yaseen and Ravinesh Deo - Predictive Modeling of Oscillating Plasma Energy Release for Clean Combustion Engines
Ming Zheng and Ramendra Prasad - Nowcasting solar irradiance for effective solar power plants operation and smart grid management
Viorel Badescu - Short-term energy demand modelling with hybrid emotional neural networks integrated with genetic algorithm
Sagthitharan Karalasingham, Ravinesh Deo and Ramendra Prasad - Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System in energy modeling of agricultural products
Ashkan Nabavi-Pelesaraei - Support Vector Machine Models for Multi-Step Wind Speed Forecasting
Shobna Prasad, Thong Nguyen-Huy and Ravinesh Deo - MARS Model for Prediction of Short and Long-term Global Solar Radiation
L.J.M. Deilki Tharaka Balalla, Thong Nguyen-Huy and Ravinesh Deo - Wind Speed Forecasting in Nepal using Self Organizing Map-based Online Sequential Extreme Learning Machine (SOM-OSELM)
Neelesh Sharma and Ravinesh Deo - Potential growth in small-scale distributed generation systems in Brazilian capitals
Julio Cezar M. Siluk - The trend of Energy Consumption in Developing Nations for the last two decades: A case study from a statistical perspective
Anshuman Dey Kirty
- No. of pages: 552
- Language: English
- Edition: 1
- Published: September 30, 2020
- Imprint: Elsevier
- Paperback ISBN: 9780128177723
- eBook ISBN: 9780128177730
RD
Ravinesh Deo
Professor Ravinesh Deo is an Associate Professor at University of Southern Queensland, Australia, Program Director for Postgraduate Science Program and Research Leader in Artificial Intelligence. He also serves as Associate Editor for two international journals: Stochastic Environmental Research and Risk Assessment and the ASCE Journal Hydrologic Engineering journal (USA). As an Applied Data Scientist with proven leadership in artificial intelligence, his research develops decision-systems with machine learning, heuristic and metaheuristic algorithms to improve real-life predictive systems especially using deep learning explainable AI, convolutional neural networks and long short-term memory networks. He was awarded internationally competitive fellowships including Queensland Government U.S. Smithsonian Fellowship, Australia-India Strategic Fellowship, Australia-China Young Scientist Exchange Award, Japan Society for Promotion of Science Fellowship, Chinese Academy of Science Presidential International Fellowship and Endeavour Fellowship. He is a member of scientific bodies, won Publication Excellence Awards, Head of Department Research Award, Dean’s Commendation for Postgraduate Supervision, BSc Gold Medal for Academic Excellence and he was the Dux of Fiji in Year 13 examinations. Professor Deo held visiting positions at United States Tropical Research Institute, Chinese Academy of Science, Peking University, Northwest Normal University, University of Tokyo, Kyoto and Kyushu University, University of Alcala Spain, McGill University and National University of Singapore. He has undertaken knowledge exchange programs in Singapore, Japan, Europe, China, USA and Canada and secured international standing by researching innovative problems with global researchers. He has published Books with Springer Nature, Elsevier and IGI and over 190 publications of which over 140 are Q1 including refereed conferences, Edited Books and book chapters. Professor Deo’s papers have been cited over 4,000 times with Google Scholar H-Index of 36 and a Field Weighted Citation Index exceeding 3.5.
Affiliations and expertise
Associate Professor, University of Southern Queensland, Australia, Program Director for Postgraduate Science Program and Research Leader in Artificial IntelligencePS
Pijush Samui
Dr. Samui is an Associate Professor in the Department of Civil Engineering at NIT Patna, India. He received his PhD in Geotechnical Engineering from the Indian Institute of Science Bangalore, India, in 2008. His research interests include geohazard, earthquake engineering, concrete technology, pile foundation and slope stability, and application of AI for solving different problems in civil engineering. Dr. Samui is a repeat Elsevier editor but also a prolific contributor to journal papers, book chapters, and peer-reviewed conference proceedings.
Affiliations and expertise
Associate Professor, Dept. of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, IndiaSR
Sanjiban Sekhar Roy
Dr. Sanjiban Sekhar Roy (Member, IEEE) is a distinguished academic and researcher, currently serving as a Professor in the School of Computer Science and Engineering at Vellore Institute of Technology (VIT). He earned his Ph.D. in 2016 from VIT, and from 2019 to 2020, he served as an Associate Researcher at Ton Duc Thang University, Vietnam. With an extensive academic career, Dr. Roy has published over 80 peer-reviewed articles in renowned international journals and conferences, making significant contributions to the fields of deep learning, advanced machine learning, and artificial intelligence. He has authored and co-authored several books published by Elsevier and CRC Press. In addition to these, he has edited 10 books with prestigious international publishers, demonstrating his expertise in computer science and technology. Dr. Roy holds two patents and is an active member of various doctoral committees, providing valuable guidance to Ph.D. scholars. He has mentored numerous postgraduate and undergraduate students, helping them navigate their research projects and academic pursuits. Beyond his research and teaching, Dr. Sanjiban Sekhar Roy has served as an editorial member for several highly respected journals and has edited special issues for prominent publications in his field. His research and academic contributions have been recognized globally, earning him the prestigious “Diploma of Excellence” Award for academic research from the Ministry of National Education, Romania, in 2019. Dr. Roy’s work continues to push the boundaries of artificial intelligence, particularly in deep learning and machine learning. His contributions to the academic community and his leadership in research have made a lasting impact on the advancement of these transformative technologies.
Affiliations and expertise
School of Computer Science and Engineering, Vellore Institute of TechnologyRead Predictive Modelling for Energy Management and Power Systems Engineering on ScienceDirect