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230618s2023 sz a o 001 0 eng d |
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|a YDX
|b eng
|e rda
|e pn
|c YDX
|d GW5XE
|d EBLCP
|d OCLCF
|d OCLCQ
|d OCLCO
|d UKAHL
|d OCLCL
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| 019 |
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|a 1382693440
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|a 9783031264962
|q (electronic bk.)
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|a 3031264967
|q (electronic bk.)
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|z 9783031264955
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|z 3031264959
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| 024 |
7 |
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|a 10.1007/978-3-031-26496-2
|2 doi
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| 035 |
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|a (OCoLC)1382690149
|z (OCoLC)1382693440
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| 050 |
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4 |
|a TJ808
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| 049 |
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|a HCDD
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| 245 |
0 |
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|a Advances in artificial intelligence for renewable energy systems and energy autonomy /
|c Mukhdeep Singh Manshahia, Valeriy Kharchenko, Gerhard-Wilhelm Weber, Pandian Vasant, editors.
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| 264 |
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|a Cham :
|b Springer,
|c [2023]
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| 264 |
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|c ©2023
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| 300 |
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|a 1 online resource (xxii, 285 pages) :
|b illustrations (chiefly color).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a EAI/Springer innovations in communication and computing
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| 500 |
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|a Includes index.
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| 520 |
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|a This book provides readers with emerging research that explores the theoretical and practical aspects of implementing new and innovative artificial intelligence (AI) techniques for renewable energy systems. The contributions offer broad coverage on economic and promotion policies of renewable energy and energy-efficiency technologies, the emerging fields of neuro-computational models and simulations under uncertainty (such as fuzzy-based computational models and fuzzy trace theory), evolutionary computation, metaheuristics, machine learning applications, advanced optimization, and stochastic models. This book is a pivotal reference for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in artificial intelligence, renewable energy systems, and energy autonomy. Based on sustainability as a fundamental factor for intelligent computing; Focuses on the role AI plays in smart living, energy transition, and sustainable development; Covers a broad range of green energy-related topics.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed June 26, 2023).
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| 505 |
0 |
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|a Intro -- Foreword -- Preface -- Acknowledgment -- Contents -- About the Editors -- General Approaches to Assessing Electrical Load of Agro-industrial Complex Facilities When Justifying the Parameters of the Photovoltaic Power System -- 1 Introduction -- 2 Materials and Methods -- 3 Results and Discussion -- 3.1 Construction of Annual and Daily Charts of Electric Loads by the Calculation Method -- 3.2 Construction of Annual and Daily Schedules of Electrical Loads According to the Guidelines
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| 505 |
8 |
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|a 3.3 Construction of Annual and Daily Schedules of Electrical Loads by Monitoring the Actual Consumed Electricity -- 3.4 Analysis of Operation Modes of Photovoltaic Modules Considering Load Graph of the Consumer -- 4 Conclusions -- References -- RBFNN for MPPT Controller in Wind Energy Harvesting System -- 1 Introduction -- 2 Wind Energy Harvesting System -- 2.1 Model of Wind Turbine -- 2.2 Modeling of the PMSG -- 3 Proposed Control Strategies -- 3.1 Radial Basis Function (RBF) -- 4 Simulation Results and Discussion -- 5 Conclusion -- References
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| 505 |
8 |
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|a Simulation Optimum Performance All-Wheels Plug-In Hybrid Electric Vehicle -- 1 Introduction -- 2 Plug-In Hybrid Electric Vehicle Components -- 3 Plug-In Hybrid Electric Control Methodology -- 3.1 EV Mode -- 3.2 Series Mode -- 3.3 Parallel Mode -- 3.4 Recuperation Brake Energy Mode -- 3.5 Motor Start/Stop Automatic (MSA) Mode -- 3.6 Freewheeling Mode -- 3.7 Mechanical Braking Mode -- 4 AWD-PHEV Plant Model -- 5 Application -- 5.1 Simulation Results -- 6 Conclusions -- References -- Artificial Intelligence Application to Flexibility Provision in Energy Management System: A Survey -- 1 Introduction
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| 505 |
8 |
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|a 2 Conventional Approach to Flexibility Management -- 2.1 Demand-Side (Load) Management -- 2.2 Energy Storage Systems (ESSs) -- 2.3 Electric Vehicles: V2G and G2V Technologies -- 2.4 Grid Reinforcement -- 3 Review of Artificial Intelligence and Its Application to Flexibility Management in Energy System -- 4 Planning Integrated Flexibility Management with Artificial Intelligence -- 5 Conclusions -- References -- Machine Learning Applications for Renewable Energy Systems -- 1 Introduction -- 2 Related Work -- 3 Key Applications -- 3.1 Forecasting -- 3.1.1 Weather Forecasting
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| 505 |
8 |
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|a 3.1.2 Wind and Solar Power Production Forecasting -- 3.1.3 Load Forecasting -- 3.2 Integrating AI with Smart Grids -- 3.2.1 Applications of AI in Smart Grids -- 3.3 Condition Monitoring and Fault Prognostics of Renewable Energy Systems -- 3.3.1 Hydropower Projects -- 3.3.2 Wind Power Projects -- 3.3.3 Solar Power Projects -- 4 Resources (ML Algorithms and Datasets) -- 4.1 AI/ML Algorithms -- 4.1.1 Fuzzy Logic -- 4.1.2 Hidden Markov Models (HMMs) -- 4.1.3 Conventional ML Algorithms -- 4.1.4 Artificial Neural Networks (ANNs) -- 4.2 Datasets -- 4.2.1 Forecasting Energy Supply, Demand, and Weather
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| 650 |
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0 |
|a Renewable energy sources
|x Technological innovations.
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| 650 |
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0 |
|a Artificial intelligence
|x Environmental aspects.
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| 650 |
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7 |
|a Renewable energy sources
|x Technological innovations
|2 fast
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| 700 |
1 |
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|a Manshahia, Mukhdeep Singh,
|e editor.
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| 700 |
1 |
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|a Kharchenko, Valeriy,
|d 1938-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PBJvmQYKVtXqVd4Yyk8KGHC
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| 700 |
1 |
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|a Weber, Gerhard-Wilhelm,
|e editor.
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| 700 |
1 |
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|a Vasant, Pandian,
|e editor.
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| 776 |
0 |
8 |
|c Original
|z 3031264959
|z 9783031264955
|w (OCoLC)1363101484
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| 830 |
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0 |
|a EAI/Springer innovations in communication and computing.
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| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-26496-2
|y Click for online access
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| 903 |
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|a SPRING-ALL2023
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| 994 |
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|a 92
|b HCD
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