Data-driven Optimization and Control for Autonomous Energy Systems

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Éditeur :

Springer


Paru le : 2025-10-19

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Description

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.
Pages
156 pages
Collection
n.c
Parution
2025-10-19
Marque
Springer
EAN papier
9789819517817
EAN PDF
9789819517824

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
15
Taille du fichier
14162 Ko
Prix
168,79 €
EAN EPUB
9789819517824

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
15
Taille du fichier
26235 Ko
Prix
168,79 €

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