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 promote their potential applications in the power and energy sectors. Its key features include:
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An exclusive section on essential preprocessing approaches for the data-driven model,
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A detailed overview of data-driven model applications to power system planning, and operational activities,
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Specific focus on developing forecasting models for renewable generations such as solar PV and wind power and
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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.
Preface
Chapter 1 : Preprocessing Approaches for Data-Driven Modeling
Chapter 2 : Power System Planning Using Data-Driven Models
Chapter 3 : Data-Driven Analytics for Power System Stability Assessment
Chapter 4 : Data-Driven Machine Learning Models for Load Power Forecasting in Photovoltaic systems
Chapter 5 : Forecasting of Renewable Energy Using Fractional-Order Neural Networks
Chapter 6 : Data-Driven Photovoltaic System Characteristic Determination using Nonlinear System Identification
Chapter 7 : Fractional Feedforward Neural Network-Based Smart Grid Stability Prediction Model
Chapter 8 : Data-driven Optimization Framework for Microgrid Energy Management Considering Demand Response and Generation Uncertainties
Chapter 9 : Optimization of Controllers for Sustained Building
Chapter 10 : Intelligent Data : Driven Approach for Fractional-Order Wireless Power Transfer System
Index
