Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives
Editat de Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen, Kayal Padmanandam, Rajesh Kumar Dhanaraj, Balamurugan Balusamyen Limba Engleză Paperback – 23 sep 2022
Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector.
- Helps improve the prediction capability of AI algorithms to make evidence-based decisions in the smart supply of electricity, including load shedding
- Focuses on how to integrate AI and ML into the energy sector in the real-world, with many chapters accompanied by case studies
- Addresses a number of proven AI and ML- informed techniques in rebalancing supply and demand
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Specificații
ISBN-13: 9780323916646
ISBN-10: 0323916643
Pagini: 226
Ilustrații: Approx. 100 illustrations
Dimensiuni: 152 x 229 x 17 mm
Greutate: 0.31 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0323916643
Pagini: 226
Ilustrații: Approx. 100 illustrations
Dimensiuni: 152 x 229 x 17 mm
Greutate: 0.31 kg
Editura: ELSEVIER SCIENCE
Cuprins
1. Introduction: Artificial intelligence and Smart Power Systems
2. Integrated Architecture of Machine Learning and Smart Power System
3. Challenges and issues in Power Systems
4. Load shedding and related techniques to solve the power crisis
5. ML in distributed energy resources and prosumers market
6. ML-based electricity demand prediction
7. Applying ML to determine the power outage
8. Predictive and Prescriptive analytics for component fault detection
9. Balancing demand and supply of electricity with machine learning
10. Preventive care of grid hardware with anomaly detection
11. AI-based Smart feeder monitoring system
12. Algorithms for buss loss and reliability indices calculations
13. ML-based security solutions to protect smart power systems
14. Cyber-attacks ,security data detection, and critical loads in the power systems
15. Integration of AI/ML into the energy sector: Case Studies
2. Integrated Architecture of Machine Learning and Smart Power System
3. Challenges and issues in Power Systems
4. Load shedding and related techniques to solve the power crisis
5. ML in distributed energy resources and prosumers market
6. ML-based electricity demand prediction
7. Applying ML to determine the power outage
8. Predictive and Prescriptive analytics for component fault detection
9. Balancing demand and supply of electricity with machine learning
10. Preventive care of grid hardware with anomaly detection
11. AI-based Smart feeder monitoring system
12. Algorithms for buss loss and reliability indices calculations
13. ML-based security solutions to protect smart power systems
14. Cyber-attacks ,security data detection, and critical loads in the power systems
15. Integration of AI/ML into the energy sector: Case Studies