Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications

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