LEADER 04431nam 22006615 450 001 9910988385203321 005 20250322115429.0 010 $a3-031-85209-5 024 7 $a10.1007/978-3-031-85209-1 035 $a(CKB)38111377900041 035 $a(DE-He213)978-3-031-85209-1 035 $a(MiAaPQ)EBC31973477 035 $a(Au-PeEL)EBL31973477 035 $a(EXLCZ)9938111377900041 100 $a20250322d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence for Energy Systems $eDriving Intelligent, Flexible and Optimal Energy Management /$fby Elissaios Sarmas, Vangelis Marinakis, Haris Doukas 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XVII, 266 p. 49 illus., 40 illus. in color.) 225 1 $aLearning and Analytics in Intelligent Systems,$x2662-3455 ;$v46 311 08$a3-031-85208-7 327 $a1.The Climate Crisis and the Four Pillars of Energy Transition: Decarbonization, Digitization, Decentralization, and Democratization -- 2.The Role of Artificial Intelligence in Transforming the Energy Sector: A Comprehensive Review -- 3.Scalable Framework for Intelligent System Architecture to Address Challenges in the Energy Sector -- 4.Deep Learning Models for Short-Term Forecasting of Photovoltaic Energy Production -- 5.Machine Learning-Driven Energy Consumption Forecasting for Building Profiling -- 6.Meta-Learning Approaches for Assessing Energy Efficiency Investments in Buildings -- 7.Ensemble Machine Learning Models for Estimating Energy Savings from Efficiency Measures in Buildings -- 8.Optimization Model for Scheduling Flexible Loads to Mitigate Energy Peaks -- 9.Optimization Model for Electric Vehicle Integration and Energy Storage to Achieve Energy Autonomy -- 10.Future Directions of Intelligent Energy Management and the Role of Generative AI. 330 $aThis book focuses on creating an integrated library of learning models and optimization techniques to assist decision-making on issues in the energy and building sector. It provides modern solutions to energy management and efficiency while addressing a scientific gap in the development of advanced algorithmic methods to solve these problems. More specifically, the focus is on the development of models and algorithms for problems falling into three broader categories, namely: (a) Distributed Energy Generation, (b) Microgrid Flexibility, and (c) Building Energy Efficiency. Artificial Intelligence models and mathematical optimization techniques are developed and presented for applications related to each of these categories, through a thorough analysis of the fundamental parameters of each application as well as the interactions among them. Professors, researchers, scientists, engineers, and students in energy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research. 410 0$aLearning and Analytics in Intelligent Systems,$x2662-3455 ;$v46 606 $aComputational intelligence 606 $aEngineering$xData processing 606 $aEnergy policy 606 $aEnergy policy 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aData Engineering 606 $aEnergy Policy, Economics and Management 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aEngineering$xData processing. 615 0$aEnergy policy. 615 0$aEnergy policy. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aData Engineering. 615 24$aEnergy Policy, Economics and Management. 615 24$aArtificial Intelligence. 676 $a006.3 700 $aSarmas$b Elissaios$4aut$4http://id.loc.gov/vocabulary/relators/aut$01065691 702 $aMarinakis$b Vangelis$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aDoukas$b Haris$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910988385203321 996 $aArtificial Intelligence for Energy Systems$94350043 997 $aUNINA