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Predictive Modelling for Energy Management and Power Systems Engineering - 1st Edition - ISBN: 9780128177723

Predictive Modelling for Energy Management and Power Systems Engineering

1st Edition

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Editors: Ravinesh Deo Pijush Samui Sanjiban Sekhar Roy
Paperback ISBN: 9780128177723
Imprint: Elsevier
Published Date: 1st October 2020
Page Count: 400
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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. 

Key Features

  • 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

Table of Contents

  1. A Multi-Objective Optimal VAR Dispatch Using FACTS Devices Considering Voltage Stability and Contingency Analysis
  2. PV panels lifespan increase by control
    Bechara NEHME
  3. Community-scale rural energy systems: General planning algorithms and management methods in developing countries
    A. López-González
  4. Proven ESS Applications for Power System Stability and Transition Issues
    Jean Ubertalli
  5. Forecasting solar radiation with evolutionary polynomial regression, wavelet transform & ensemble empirical mode decomposition
    Mohammad Rezaie-Balf, Sungwon Kim, Alireza Ghaemi and Ravinesh C. Deo
  6. Development and Comparison of Data-driven Models for Wind Speed Forecasting in Australia
    Ananta Neupane, Nawin Raj, Ravinesh Deo and Mumtaz Ali
  7. Modelling Photosynthetic Active Radiation with a Hybrid Multilayer Perceptron-Firefly Optimizer Algorithm
    Harshna Lata Gounder, Zaher Munder Yaseen and Ravinesh Deo
  8. Predictive Modeling of Oscillating Plasma Energy Release for Clean Combustion Engines
    Ming Zheng and Ramendra Prasad
  9. Nowcasting solar irradiance for effective solar power plants operation and smart grid management
    Viorel Badescu
  10. Short-term energy demand modelling with hybrid emotional neural networks integrated with genetic algorithm
    Sagthitharan Karalasingham, Ravinesh Deo and Ramendra Prasad
  11. Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System in energy modeling of agricultural products
    Ashkan Nabavi-Pelesaraei
  12. Support Vector Machine Models for Multi-Step Wind Speed Forecasting
    Shobna Prasad, Thong Nguyen-Huy and Ravinesh Deo
  13. MARS Model for Prediction of Short and Long-term Global Solar Radiation
    L.J.M. Deilki Tharaka Balalla, Thong Nguyen-Huy and Ravinesh Deo
  14. Wind Speed Forecasting in Nepal using Self Organizing Map-based Online Sequential Extreme Learning Machine (SOM-OSELM)
    Neelesh Sharma and Ravinesh Deo
  15. Potential growth in small-scale distributed generation systems in Brazilian capitals
    Julio Cezar M. Siluk
  16. 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
1st October 2020
Paperback ISBN:

About the Editors

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 at University of Southern Queensland, Australia, Program Director for Postgraduate Science Program and Research Leader in Artificial Intelligence

Pijush Samui

Professor Pijush Samui is currently an 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. He developed a new method for prediction of response of soil during an earthquake. He has given charts for prediction of response of soil during an earthquake and developed equations for prediction of lateral spreading of soil due to earthquake. He developed equations for determination of seismic liquefaction potential of soil based on strain energy and prediction of seismic attenuation. He developed efficient models for prediction of magnitude of reservoir induced earthquake. He has developed models for determination of medical waste generation in hospitals with equations used for practical purpose. The developed models can be used for clean India project. He determined frequency effects on liquefaction by using Shake Table. He has applied AI techniques for determination of bearing capacity and settlement of foundation and equations for determination of bearing capacity and settlement of shallow foundation. He also developed equations for determination of compression index and angle of shearing resistance of soil. he developed equations for prediction of uplift capacity of suction caisson. He also developed equations for determination of fracture parameters of concrete. His active research activity is evident from his extensive citation of publications in google scholar (total frequency of 1280) with H-Index of 22. Dr Samui has published journal papers, books/book chapters and peer reviewed conference papers that involved co-authors from Australia, India, Korea and several other nations. He is also holding the position of Visiting Professor at Far East Federal University (Russia).

Affiliations and Expertise

Associate Professor, National Institute of Technology, Patna, India

Sanjiban Sekhar Roy

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.

Affiliations and Expertise

Associate Professor in School of Computer Science and Engineering, Vellore Institute of Technology

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