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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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Indragandhi, V. (Editor), Elakkiya, R. (Editor), Subramaniyaswamy, V. (Editor)
Format:	eBook
Language:	English
Published:	Amsterdam, Netherlands : Elsevier, [2025]
Series:	Advances in Intelligent Energy Systems
Subjects:	Smart power grids > Data processing. Machine learning. Big data. Réseaux électriques intelligents > Informatique. Apprentissage automatique. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.
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.
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.
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 &amp
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.
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.