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Optimization, uncertainty and machine learning in wind energy conversion systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization, uncertainty and machine learning in wind energy conversion systems/ edited by Kishalay Mitra, Richard Everson, Jonathan Fieldsend.
other author:	Mitra, Kishalay.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxi, 266 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Part 1 State-of-the-art in Optimization, Uncertainty handling, Machine Learning methods, and Wake models -- Chapter 1. Introduction -- Chpater 2. Multi-objective optimisation with uncertainty: considerations for wind farm optimisation -- Chapter 3. Offline Multi-Objective Optimisation using Surrogate-Assisted Evolutionary Algorithms with Uncertainty Quantification -- Chapter 4. Bayesian optimisation for expensive computational fluid dynamics design problems -- Chapter 5. Multidisciplinary uncertainty modelling using Copulas.
Contained By:	Springer Nature eBook
Subject:	Wind energy conversion systems. -
Online resource:	https://doi.org/10.1007/978-981-97-7909-3
ISBN:	9789819779093

Optimization, uncertainty and machine learning in wind energy conversion systems
Optimization, uncertainty and machine learning in wind energy conversion systems[electronic resource] /edited by Kishalay Mitra, Richard Everson, Jonathan Fieldsend. - Singapore :Springer Nature Singapore :2025. - xxi, 266 p. :ill. (some col.), digital ;24 cm. - Engineering optimization: methods and applications,2731-4057. - Engineering optimization: methods and applications..

Part 1 State-of-the-art in Optimization, Uncertainty handling, Machine Learning methods, and Wake models -- Chapter 1. Introduction -- Chpater 2. Multi-objective optimisation with uncertainty: considerations for wind farm optimisation -- Chapter 3. Offline Multi-Objective Optimisation using Surrogate-Assisted Evolutionary Algorithms with Uncertainty Quantification -- Chapter 4. Bayesian optimisation for expensive computational fluid dynamics design problems -- Chapter 5. Multidisciplinary uncertainty modelling using Copulas.

This book presents state-of-the-art technologies in wind farm layout optimization and control to improve the current industry/research practice. The contents take readers towards a different kind of uncertainty handling through the discussion on several techniques enabling maximum energy harnessing out of uncertain situations. The book aims to give a detailed overview of such concepts in the first part, where the recent advancements in the fields of (i) Wind farm layout optimization, (ii) Multi-objective Optimization and Uncertainty handling in optimization methods, (iii) Development of Machine Learning-based surrogate models in optimization, and (iv) Different types of wake models for wind farms will be discussed. The second part will cover the application of the aforementioned techniques on the wind farm layout optimization and control through several chapters such as (i) Wind farm performance assessment using Computational Fluid Dynamics (CFD) tools, (ii) Artificial Neural Network (ANN) based hybrid wake models, (iii) Long Short-term Memory (LSTM) & Support Vector Regression (SVR) based forecasting and micro-siting, (iv) windfarm micro-siting using data-driven Robust Optimization (RO) as well as Generative Adversarial Networks (GANs), (v) Reinforcement learning (RL) based wind farm control and (vi) Application of eXplainable AI (XAI) tools for interpreting wind time-series data. In this manner, the book provides state-of-the-art techniques in the fields of multi-objective optimization, Evolutionary Algorithms, Machine Learning surrogate models, Bayesian Optimization, Data Analysis, and Optimization under Uncertainty and their applications in the field of wind energy generation that can be extremely generic and can be applied to many other engineering fields. This volume will be of interest to those in academia and industry.

ISBN: 9789819779093

Standard No.: 10.1007/978-981-97-7909-3doiSubjects--Topical Terms:

814623
Wind energy conversion systems.

LC Class. No.: TK1541

Dewey Class. No.: 621.312136

Optimization, uncertainty and machine learning in wind energy conversion systems
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245 0 0 $a Optimization, uncertainty and machine learning in wind energy conversion systems $h [electronic resource] / $c edited by Kishalay Mitra, Richard Everson, Jonathan Fieldsend.
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490 1 $a Engineering optimization: methods and applications, $x 2731-4057
505 0 $a Part 1 State-of-the-art in Optimization, Uncertainty handling, Machine Learning methods, and Wake models -- Chapter 1. Introduction -- Chpater 2. Multi-objective optimisation with uncertainty: considerations for wind farm optimisation -- Chapter 3. Offline Multi-Objective Optimisation using Surrogate-Assisted Evolutionary Algorithms with Uncertainty Quantification -- Chapter 4. Bayesian optimisation for expensive computational fluid dynamics design problems -- Chapter 5. Multidisciplinary uncertainty modelling using Copulas.
520 $a This book presents state-of-the-art technologies in wind farm layout optimization and control to improve the current industry/research practice. The contents take readers towards a different kind of uncertainty handling through the discussion on several techniques enabling maximum energy harnessing out of uncertain situations. The book aims to give a detailed overview of such concepts in the first part, where the recent advancements in the fields of (i) Wind farm layout optimization, (ii) Multi-objective Optimization and Uncertainty handling in optimization methods, (iii) Development of Machine Learning-based surrogate models in optimization, and (iv) Different types of wake models for wind farms will be discussed. The second part will cover the application of the aforementioned techniques on the wind farm layout optimization and control through several chapters such as (i) Wind farm performance assessment using Computational Fluid Dynamics (CFD) tools, (ii) Artificial Neural Network (ANN) based hybrid wake models, (iii) Long Short-term Memory (LSTM) & Support Vector Regression (SVR) based forecasting and micro-siting, (iv) windfarm micro-siting using data-driven Robust Optimization (RO) as well as Generative Adversarial Networks (GANs), (v) Reinforcement learning (RL) based wind farm control and (vi) Application of eXplainable AI (XAI) tools for interpreting wind time-series data. In this manner, the book provides state-of-the-art techniques in the fields of multi-objective optimization, Evolutionary Algorithms, Machine Learning surrogate models, Bayesian Optimization, Data Analysis, and Optimization under Uncertainty and their applications in the field of wind energy generation that can be extremely generic and can be applied to many other engineering fields. This volume will be of interest to those in academia and industry.
650 0 $a Wind energy conversion systems. $3 814623
650 0 $a Machine learning. $3 533906
650 0 $a Artificial intelligence $x Engineering applications. $3 1567162
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Optimization. $3 891104
650 2 4 $a Wind Energy. $3 3591936
700 1 $a Mitra, Kishalay. $3 3781877
700 1 $a Everson, Richard. $3 3781878
700 1 $a Fieldsend, Jonathan. $3 3781879
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Engineering optimization: methods and applications. $3 3625605
856 4 0 $u https://doi.org/10.1007/978-981-97-7909-3
950 $a Energy (SpringerNature-40367)