Kishore Bingi – författare

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 modela detailed overview of data-driven model applications to power system planning and operational activitiesspecific focus on developing forecasting models for renewable generations such as solar PV and wind power, andshowcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworksThis 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.

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 modela detailed overview of data-driven model applications to power system planning and operational activitiesspecific focus on developing forecasting models for renewable generations such as solar PV and wind power, andshowcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworksThis 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.

This book presents a detailed study on fractional-order, set-point, weighted PID control strategies and the development of curve-fitting-based approximation techniques for fractional-order parameters.

This book presents a detailed study on fractional-order, set-point, weighted PID control strategies and the development of curve-fitting-based approximation techniques for fractional-order parameters.

Therefore, to address these issues, the book proposes control strategies that retain the simplicity of PID in terms of ease of tuning and structure, while improving on the performance of the closed-loop system with regard to stochastic network delays and mismatches.

Therefore, to address these issues, the book proposes control strategies that retain the simplicity of PID in terms of ease of tuning and structure, while improving on the performance of the closed-loop system with regard to stochastic network delays and mismatches.

mso-fareast-language: EN-IN;">This book suggests the development of single and multi-layer fractional-order neural networks that incorporate fractional-order activation functions derived using fractional-order derivatives.

The book investigates the fractional calculus-based approaches and their benefits to adopting in complex real-time areas. Researchers from academia and industry working or starting research in applied fractional calculus methods will find the book most useful.

The book investigates the fractional calculus-based approaches and their benefits to adopting in complex real-time areas. Researchers from academia and industry working or starting research in applied fractional calculus methods will find the book most useful.

Two fractional-order PI controllers are proposed and designed: fractional-order predictive PI and hybrid iterative learning based fractional-order predictive PI controller.

Two fractional-order PI controllers are proposed and designed: fractional-order predictive PI and hybrid iterative learning based fractional-order predictive PI controller.

This book offers a wide range of interesting correlations beyond the domains of dynamical systems, complex systems, and fractal geometry.