02742nam 22005415 450 991103493990332120251020130402.0981-9517-82-610.1007/978-981-95-1782-4(MiAaPQ)EBC32364747(Au-PeEL)EBL32364747(CKB)41689402500041(DE-He213)978-981-95-1782-4(EXLCZ)994168940250004120251020d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierData-driven Optimization and Control for Autonomous Energy Systems /by Gang Wang, Jian Sun, Jie Chen1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (259 pages)Energy Series981-9517-81-8 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.Energy SeriesElectric power productionAutomationMechanical Power EngineeringAutomationElectric power production.Automation.Mechanical Power Engineering.Automation.621.31Wang Gang1853399Sun Jian1834768Chen Jie1299851MiAaPQMiAaPQMiAaPQBOOK9911034939903321Data-Driven Optimization and Control for Autonomous Energy Systems4449508UNINA