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Data-driven optimization and control for autonomous energy systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven optimization and control for autonomous energy systemsby Gang Wang, Jian Sun, Jie Chen.
Author:	Wang, Gang.
other author:	Sun, Jian.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	vii, 156 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Electric power systemsAutomation.
Online resource:	https://doi.org/10.1007/978-981-95-1782-4
ISBN:	9789819517824$q(electronic bk.)

Data-driven optimization and control for autonomous energy systems
Wang, Gang.

Data-driven optimization and control for autonomous energy systems[electronic resource] /by Gang Wang, Jian Sun, Jie Chen. - Singapore :Springer Nature Singapore :2025. - vii, 156 p. :ill., digital ;24 cm.

Introduction -- State Estimation via Composite Optimization -- State Estimation from Rank One Measurements -- State Estimation and Forecasting via Deep Unrolled Neutral Networks -- Data Graph Prior for State Estimation -- Stochastic Optimization -- Conclusion.

This book introduces a pioneering framework for monitoring and controlling autonomous energy systems, distinguished by its use of physics-informed deep neural networks. These networks provide accurate estimations and forecasts, interlacing with advanced composite optimization algorithms to simplify the complex processes of state estimation. This approach not only boosts operational efficiency but also maximizes flexibility through a data-driven methodology integrated with physics-based principles. The framework leverages the power of neural networks to define the intricate relationship between system states and control policies, offering precise, robust control strategies that adapt to dynamically changing system conditions. This book is essential reading for professionals looking to enhance the performance and flexibility of energy systems through cutting-edge technology.

ISBN: 9789819517824$q(electronic bk.)

Standard No.: 10.1007/978-981-95-1782-4doiSubjects--Topical Terms:

292589
Electric power systems
--Automation.

LC Class. No.: TK1005

Dewey Class. No.: 621.317

Data-driven optimization and control for autonomous energy systems
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245 1 0 $a Data-driven optimization and control for autonomous energy systems $h [electronic resource] / $c by Gang Wang, Jian Sun, Jie Chen.
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300 $a vii, 156 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- State Estimation via Composite Optimization -- State Estimation from Rank One Measurements -- State Estimation and Forecasting via Deep Unrolled Neutral Networks -- Data Graph Prior for State Estimation -- Stochastic Optimization -- Conclusion.
520 $a This book introduces a pioneering framework for monitoring and controlling autonomous energy systems, distinguished by its use of physics-informed deep neural networks. These networks provide accurate estimations and forecasts, interlacing with advanced composite optimization algorithms to simplify the complex processes of state estimation. This approach not only boosts operational efficiency but also maximizes flexibility through a data-driven methodology integrated with physics-based principles. The framework leverages the power of neural networks to define the intricate relationship between system states and control policies, offering precise, robust control strategies that adapt to dynamically changing system conditions. This book is essential reading for professionals looking to enhance the performance and flexibility of energy systems through cutting-edge technology.
650 0 $a Electric power systems $x Automation. $3 292589
650 0 $a Electric power systems $x Control. $3 224373
650 1 4 $a Mechanical Power Engineering. $3 919412
650 2 4 $a Automation. $3 190451
700 1 $a Sun, Jian. $3 1005654
700 1 $a Chen, Jie. $3 558796
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856 4 0 $u https://doi.org/10.1007/978-981-95-1782-4
950 $a Energy (SpringerNature-40367)

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