Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications Intelligent Data-Driven Systems and Artificial Intelligence Series

This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include:

• an exclusive section on essential preprocessing approaches for the data-driven model
• a detailed overview of data-driven model applications to power system planning and operational activities
• specific focus on developing forecasting models for renewable generations such as solar PV and wind power, and
• showcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworks

This book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.

1. Preprocessing Approaches for Data-Driven Modeling. 2. Power System Planning Using Data-Driven Models. 3. Data-Driven Analytics for Power System Stability Assessment. 4. Data-Driven Machine Learning Models for Load Power Forecasting in Photovoltaic systems. 5. Forecasting of Renewable Energy Using Fractional-Order Neural Networks. 6. Data-Driven Photovoltaic System Characteristic Determination using Nonlinear System Identification. 7. Fractional Feedforward Neural Network-Based Smart Grid Stability Prediction Model. 8. Data-driven Optimization Framework for Microgrid Energy Management Considering Demand Response and Generation Uncertainties. 9. Optimization of Controllers for Sustained Building. 10. Intelligent Data–Driven Approach for Fractional-Order Wireless Power Transfer System

B Rajanarayan Prusty (Senior Member, IEEE) is a Professor and Associate Dean Research in the School of Engineering, Galgotias University, Greater Noida, India. He obtained his Ph.D. from the National Institute of Technology Karnataka, Surathkal. His exceptional research work during his Ph.D. has led him to win the prestigious POSOCO Power System Awards for 2019 by Power System Operation Corporation Limited in partnership with IIT Delhi. In recognition of his publications from 2017 to 2019, he was awarded the University Foundation Day Research Award 2019 from BPUT, Rourkela, Odisha. He has 30 SCI journal publications and 50 international conference publications. He has authored 10 book chapters. He has co-authored a textbook entitled Power System Analysis: Operation and Control in I. K. International Publishing House Pvt. Ltd. He has also edited two books for CRC Press. He has been an active reviewer and has reviewed more than 500 manuscripts. He is the Associate Editor of the Journal of Electrical Engineering & Technology and the International Journal of Power and Energy Systems. He is also the Academic Editor for the journals (i) Mathematical Problems in Engineering, (ii) International Transactions on Electrical Energy Systems, and (iii) Journal of Electrical and Computer Engineering. He has handled more than 200 manuscripts in the capacity of Journal Editor. His research interests include data preprocessing, time series forecasting, high-dimensional dependence modelling, and applying machine learning and probabilistic methods to power system problems.

Neeraj Gupta obtained his Ph.D. in power systems from the Indian Institute of Technology Roorkee, Roorkee, India. He is a senior member of IEEE. He was a faculty with Thapar University, from 2008 to 2009, Adani Institute of Infrastructure Engineering, Ahmedabad, India, in 2015 and NIT Hamirpur from 2015 to 2018, and