Machine learning energy systems (20 Ergebnisse)

Paperback. Zustand: Brand New. 65 pages. 6.00x0.15x9.00 inches. In Stock.

Zustand: New. In English.

Zustand: New.

Zustand: New.

Taschenbuch. Zustand: Neu. Optimization, Uncertainty and Machine Learning in Wind Energy Conversion Systems | Kishalay Mitra (u. a.) | Taschenbuch | Engineering Optimization: Methods and Applications | xxi | Englisch | 2026 | Springer | EAN 9789819779116 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Zustand: New. Introduces spatiotemporal energy sharing with new energy vehicles and human-machine interactionsDiscusses the potential for electrification and hydrogenation in integrated building-transportation systems for sustainable development.

Paperback. Zustand: Brand New. 300 pages. 8.94x5.98x0.71 inches. In Stock.

Paperback. Zustand: Brand New. 334 pages. 9.00x5.94x2.36 inches. In Stock.

Zustand: New. Outlines the challenges, opportunities, and applications for utilizing physics-aware machine learning to support renewable energy integration to the modern gridCovers a wide variety of techniques, from fundamental principles to security con.

Paperback. Zustand: Brand New. 250 pages. 9.00x6.00x9.02 inches. In Stock.

Zustand: New. In English.

Hardcover. Zustand: Brand New. 287 pages. 9.25x6.10x9.33 inches. In Stock.

Zustand: New. 2023. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New. 2024. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New. 2025. paperback. . . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. 372 pages. 10.00x7.00x10.24 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware - Physics-Aware Machine Learning for Integrated Energy Systems Management, a new release in the Advances in Intelligent Energy Systems series, guides the reader through this state-of-the-art approach to computational methods, from data input and training to application opportunities in integrated energy systems. The book begins by establishing the principles, design, and needs of integrated energy systems in the modern sustainable grid before moving into assessing aspects such as sustainability, energy storage, and physical-economic models. Detailed, step-by-step procedures for utilizing a variety of physics-aware machine learning models are provided, including reinforcement learning, feature learning, and neural networks.Supporting students, researchers, and industry engineers to make renewable-integrated grids a reality, this book is a holistic introduction to an exciting new approach in energy systems management.…

Hardcover. Zustand: gut. Optimization, Uncertainty and Machine Learning in Wind Energy Conversion Systems (Engineering Optimization: Methods and Applications) pages.