EBook For Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications 1st Edition By B Rajanarayan Prusty, Neeraj Gupta, Kishore Bingi

EBook For Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications 1st Edition By B Rajanarayan Prusty, Neeraj Gupta, Kishore Bingi

Table Of Content

Chapter 1 – Preprocessing approaches for data-driven modelling

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 non-linear system identification

Chapter 7 – Fractional feed-forward neural network-based smart grid stability prediction model

Chapter 8 – Data-driven optimization framework for microgrid energy management

Chapter 9 – Optimization of controllers for sustained building

Chapter 10 – Intelligent data-driven approach for fractional-order wireless power transfer system

Description

• This book helps readers understand how smart, data-based methods can be used to improve power and energy systems. It explains how these methods can predict renewable energy sources like solar and wind power and shows how they work with traditional models. The goal is to help people learn about using data to make better decisions in the power and energy industries.

  Key features of the book include:

  • A section on how to prepare data for building models
  • A clear explanation of how data-based models help with power system planning and operation
  • A focus on creating models to predict renewable energy like solar and wind power
  • Examples of how math tools like optimization and calculus are used in these models.