Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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 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
- A Multi-Objective Optimal VAR Dispatch Using FACTS Devices Considering Voltage Stability and Contingency Analysis
NOUR EL YAKINE KOUBA
- PV panels lifespan increase by control
- Community-scale rural energy systems: General planning algorithms and management methods in developing countries
- Proven ESS Applications for Power System Stability and Transition Issues
- 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
- 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
- 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:
- © Elsevier 2021
- 30th September 2020
- Paperback ISBN:
- eBook ISBN:
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.
Associate Professor, University of Southern Queensland, Australia, Program Director for Postgraduate Science Program and Research Leader in Artificial Intelligence
Professor Pijush Samui is Associate Professor at National Institute of Technology, Patna, India. He is an established researcher in the application of Artificial Intelligence (AI) for solving different problems in engineering. Samui has published journal articles, peer reviewed conference papers, book chapters and 4 books. He is also holding the position of Visiting Professor at Far East Federal University (Russia).
Associate Professor, National Institute of Technology, Patna, India
Sanjiban Sekhar Roy is an Associate Professor in the School of Computer Science and Engineering, Vellore Institute of Technology. He joined VIT in the year 2009 as an Asst. Professor. His research interests include Deep Learning and advanced machine learning. He has published around 50 articles in a reputed international journal (with SCI impact factors) and conferences. He also is editorial board members to a handful of international journals and reviewer to many highly reputed journals such as Neural processing letters, Springer , IEEE Access: The Multidisciplinary Open Access Journal, Computers & Security, Elsevier , International Journal of Advanced Intelligence Paradigms, Inderscience International publishers, International Journal of Artificial Intelligence and Soft Computing, Inderscience International publishers,Ad Hoc Networks, Elsevier, Evolutionary Intelligence, Springer, Journal of Ambient Intelligence and Humanized Computing, Springer, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, Springer. He uses Deep Learning and machine learning techniques to solve many complex engineering problems, especially those are related to imagery. He is specialized in deep convolutional neural networks and generative adversarial network. Dr. Roy also has edited many books with reputed interntional publishers such as elsevier,springer and IGI Global. Very recently, Ministry of National Education, Romania in collaboration with "Aurel Vlaicu" University Arad Faculty of Engineers, Romania has awarded Dr. Roy with "Diploma of Excellence" as a sign of appreciation for the special achievements obtained in the scientific research activity in 2019.
Associate Professor in School of Computer Science and Engineering, Vellore Institute of Technology
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.