Green machine learning and big data for smart grids : practices and applications /
Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept...
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| Aineistotyyppi: | Elektroninen E-kirja |
| Kieli: | englanti |
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Amsterdam, Netherlands :
Elsevier,
[2025]
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| Sarja: | Advances in Intelligent Energy Systems
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| Aiheet: | |
| Linkit: | ScienceDirect |
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| 245 | 0 | 0 | |a Green machine learning and big data for smart grids : |b practices and applications / |c edited by V. Indragandhi, R. Elakkiya and V. Subramaniyaswamy. |
| 264 | 1 | |a Amsterdam, Netherlands : |b Elsevier, |c [2025] | |
| 300 | |a 1 online resource. | ||
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| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Advances in Intelligent Energy Systems | |
| 520 | |a Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of "green" machine learning and the essential technologies for utilizing data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests. Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation. | ||
| 500 | |a Includes index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ScienceDirect, viewed November 26, 2024). | |
| 505 | 0 | |a Front Cover -- Green Machine Learning and Big Data for Smart Grids -- Copyright Page -- Contents -- List of contributors -- Preface -- 1 Introduction to smart grid and the need for green solutions -- 1.1 Introduction -- 1.1.1 Importance of smart grids in the modern energy landscape -- 1.2 Sustainable development for smart grid -- 1.2.1 The evolution of electric grids into smart grids -- 1.2.2 Why should you go green? -- 1.3 Environmental impacts of smart grid -- 1.3.1 There are many positive environmental impacts to consider -- 1.3.2 Negative environmental impacts -- 1.3.3 Mitigation strategies -- 1.4 Applying artificial intelligence and machine learning for enhancing green solutions -- 1.4.1 Decision tree -- 1.5 Strategies for implementing artificial intelligence and machine learning in smart grids -- 1.5.1 Artificial intelligence -- 1.5.2 Machine learning -- 1.6 Case studies -- 1.6.1 Examples of successful smart grid implementations -- 1.7 Conclusion -- References -- Further Reading and Useful Web Sites -- 2 Smart grid technologies through data preprocessing and feature engineering: role in demand response and green machine lea... -- 2.1 Introduction -- 2.2 Smart grids and the data revolution -- 2.2.1 Emergence of smart grids -- 2.2.2 Data avalanche and its challenges -- 2.3 Data preprocessing: unveiling the insights -- 2.3.1 Data cleaning for data integrity -- 2.3.2 The power of normalization and standardization -- 2.3.3 Transforming data for meaningful analysis -- 2.4 Smart grid at present: technical architecture -- 2.4.1 Smart distributed generation sources -- 2.4.2 Smart metering, measurement, and monitoring -- 2.4.3 Smart management of information -- 2.4.4 Smart data transmission -- 2.4.5 Smart supervision/regulation technology -- 2.4.6 Smart security system -- 2.5 Feature engineering: crafting intelligence from raw data. | |
| 505 | 8 | |a 2.6 Demand response: enabling smart energy management -- 2.7 Green machine learning: a path to sustainability -- 2.8 Case studies: realizing potential -- 2.8.1 Urban demand response implementation -- 2.8.2 Sustainable energy integration through green machine learning -- 2.8.3 Grid reliability enhancement with data preprocessing -- 2.8.4 Demand forecasting for peak load management -- 2.8.5 Customer behavior analysis and load shifting -- 2.8.6 Renewable energy integration at scale -- 2.8.6.1 Objective -- 2.8.6.2 Approach -- 2.8.6.2.1 Data preprocessing for renewable energy data -- 2.8.6.2.2 Feature engineering for energy forecasting -- 2.8.6.2.3 Green machine learning for energy allocation -- 2.8.6.3 Results -- 2.8.6.3.1 Increased renewable energy utilization -- 2.8.6.3.2 Grid stability -- 2.8.6.3.3 Reduced carbon emissions -- 2.8.6.3.4 Cost savings -- 2.9 Challenges and future prospects -- 2.10 Conclusion -- 3 An analysis of smart grid and management through data analytical models -- 3.1 Introduction -- 3.2 Methodology -- 3.2.1 Define objectives and scope -- 3.2.2 Data collection and preparation -- 3.2.3 Feature engineering -- 3.2.4 Model selection -- 3.2.5 Training and validation -- 3.2.6 Implementation of analytical models -- 3.2.7 Real-time data processing -- 3.2.8 Monitoring and evaluation -- 3.2.9 Feedback loop and iterative improvement -- 3.2.10 Documentation and communication -- 3.2.11 Security and compliance -- 3.2.12 Scalability and future-proofing -- 3.3 Setting the stage -- 3.3.1 The vital role of data analytics: illuminating complexity with actionable insights -- 3.3.2 Charting the course ahead: a primer for the journey -- 3.4 Illuminating the future-load forecasting and demand response models -- 3.5 Safeguarding reliability-unveiling anomaly detection and predictive maintenance models. | |
| 505 | 8 | |a 3.6 Converging horizons-fusion of renewable energy and grid optimization -- 3.7 Balancing the currents-unmasking voltage stability assessment and fault detection -- 3.7.1 Voltage stability assessment and fault detection -- 3.8 Unveiling personalized power-exploring customer segmentation and energy efficiency programs -- 3.9 Unmasking unfair play-harnessing data analytics to combat energy theft -- 3.10 Conclusion -- 4 Analysis and real-time implementation of power line disturbances test in smart grid -- 4.1 Introduction -- 4.2 Related work -- 4.2.1 Power quality monitoring and assessment in smart grid -- 4.2.2 Artificial intelligence for power line disturbance analysis -- 4.2.3 Power line disturbance mitigation in smart grids -- 4.3 The working mechanism of smart grids -- 4.4 Simulation of voltage disturbances -- 4.5 Comparative analysis and implementation of different ai mitigation techniques -- 4.5.1 Genetic algorithm -- 4.5.2 Particle swarm optimization -- 4.5.3 Artificial Bee Colony -- 4.6 Conclusion -- References -- 5 Energy efficiency and conservation using machine learning -- 5.1 Introduction -- 5.2 Predicting energy demand -- 5.3 Optimizing energy systems -- 5.4 Identifying energy inefficiencies -- 5.5 A case study with oneAPI -- 5.6 Conclusion -- References -- 6 Smart grid stability prediction using binary manta ray foraging-based machine learning -- 6.1 Introduction -- 6.2 Related works -- 6.3 Proposed methodology -- 6.3.1 Layered framework for the integration of internet of things and smart grids systems -- 6.3.2 Dataset used and its preprocessing -- 6.3.3 Mantra rays foraging optimization-based weighted extreme learning machine classification -- 6.3.3.1 Extreme learning machine -- 6.3.3.2 Weight selection in weighted extreme learning machine using binary mantra rays foraging optimization -- 6.3.3.2.1 MRF -- 6.3.3.2.2 Chain foraging. | |
| 505 | 8 | |a 6.3.3.2.3 Cyclone foraging -- 6.3.3.2.4 Somersault foraging -- 6.3.3.2.5 Weight selection in Weighted Extreme Learning Machine using BMRF optimization algorithm -- 6.4 Results and discussion -- 6.4.1 Experimental setup -- 6.4.2 Performance metrics -- 6.5 Conclusion -- References -- 7 CO2 emissions: machine learning models for assessing the economic & -- environmental impact of fossil fuels and electric veh... -- 7.1 Introduction -- 7.2 Literature survey -- 7.3 System architecture and workflow -- 7.3.1 Web scraping -- 7.3.2 Preprocessing -- 7.3.3 Machine learning models for classification -- 7.3.4 Analysis stage -- 7.4 Experimental investigation and findings -- 7.5 Conclusion -- References -- 8 Detection of electricity theft in Chinese power utility state grid corporation using hybrid deep learning model -- 8.1 Introduction -- 8.2 Related works -- 8.3 Proposed methodology -- 8.3.1 Dataset description -- 8.3.2 Preprocessing -- 8.3.2.1 Initial raw data preprocessing -- 8.3.3 AE-DBN classification -- 8.3.3.1 DBN -- 8.3.3.2 Deep AE -- 8.3.3.3 The proposed AE-DBN design for ETD -- 8.3.3.4 Design for a model -- 8.3.3.5 Optimal selection of parameters in AE using FHO -- 8.4 Results and discussion -- 8.4.1 Experimental setup -- 8.4.2 Performance metrics -- 8.4.3 Advantages and drawbacks of the projected work -- 8.5 Conclusion -- References -- 9 Paradigm shift from machine learning to federated learning -- 9.1 Introduction -- 9.2 Background -- 9.3 Multiple FL model -- 9.3.1 Learning model -- 9.3.1.1 Horizontal federated learning -- 9.3.1.2 Vertical FL -- 9.3.1.3 Federated transfer learning -- 9.3.2 Architecture -- 9.3.2.1 Centralized FL -- 9.3.2.2 Decentralized FL -- 9.4 Key consideration for federated learning -- 9.5 Application of FL -- 9.5.1 In Healthcare -- 9.5.2 In finance -- 9.5.3 IoT -- 9.5.4 Smart city -- 9.6 Challenges -- 9.6.1 Heterogeneous device. | |
| 505 | 8 | |a 9.6.2 Communication expensive -- 9.6.3 Data poisoning -- 9.6.4 Limited device memory -- 9.6.5 Privacy concern -- 9.7 Conclusion and future work -- References -- 10 Blockchain-backed design for an indestructible electric vehicle charging system -- 10.1 Introduction -- 10.1.1 Significance of blockchain technology -- 10.2 Blockchain high level EV charging architecture -- 10.2.1 Blockchain interlaced EV transactions -- 10.2.2 EV owner registration process -- 10.2.3 EV charging process and payment process -- 10.2.4 EV retailer registration process -- 10.3 Benefits of EV charging on blockchain -- 10.4 Discussions -- 10.5 Conclusion -- References -- 11 Thermal management of battery for electric vehicle -- 11.1 Introduction -- 11.2 Existing system -- 11.3 Proposed system -- 11.3.1 Performance enhancement -- 11.3.2 Rapid approaching -- 11.3.3 Improved dependability -- 11.4 Hardware results and discussion -- 11.5 Conclusion -- References -- 12 Optimal DG placement and FCL sizing using fuzzy-SSOA algorithm -- 12.1 Introduction -- 12.2 Problem formulation -- 12.3 Fuzzy logic -- 12.4 Index for voltage appropriate index(IVA) -- 12.5 Salp swarm optimization algorithm -- 12.6 Results and analysis -- 12.7 Conclusion -- References -- 13 Drive control strategies for PMSM drives in electric vehicles -- 13.1 Introduction -- 13.2 Vector control -- 13.3 Direct torque control -- 13.4 Proportional-integral-derivative/maximum torque per ampere controller -- 13.5 Adaptive fuzzy logic controller -- 13.6 Artificial neural networks-based control -- 13.7 Model reference adaptive current control/adaptive neuro-fuzzy inference system control -- 13.8 Conclusion -- References -- 14 Power management system for electric traction integrated with microgrid -- 14.1 Introduction -- 14.2 System description -- 14.2.1 PV cell characteristics -- 14.2.1.1 Battery -- 14.3 Proposed work. | |
| 650 | 0 | |a Smart power grids |x Data processing. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Big data. | |
| 650 | 6 | |a Réseaux électriques intelligents |0 (CaQQLa)000277171 |x Informatique. |0 (CaQQLa)201-0380011 | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 7 | |a Big data |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 700 | 1 | |a Indragandhi, V., |e editor. | |
| 700 | 1 | |a Elakkiya, R., |e editor. | |
| 700 | 1 | |a Subramaniyaswamy, V., |e editor. | |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443289514 |