Big Data Analysis for Smart Electrical Energy Distribution Systems

Erscheinungsjahr 2022
ISBN: 978-0-323-85556-3
Verlag: Elsevier Science

Buch, Englisch, 310 Seiten, Format (B × H): 152 mm x 229 mm

Big Data Analysis for Smart Electrical Energy Distribution Systems covers the application of big data analytics and techniques with selective applications for the operation, analysis, planning and design of future electrical distribution systems. The book provides data-driven applications in smart distribution systems, machine learning techniques for renewable energy predictions, and load forecasting examples for intelligent techno-economic operation and control of the network as a microgrid. This title gives those within this multidisciplinary field a comprehensive look at machine learning techniques for renewable energy prediction, demand forecasting, and intelligent techno-economic operation and control of distributed energy systems.With electricity networks changing rapidly due to the increased integration of intermittent and variable power generation from renewable energy sources, mismatch between the supply and demand of electricity is also on the rise. Hence, the use of new renewables is a widely discussed topic.

Zielgruppe

<p>Primary: Post grads and researchers within the area of multi-discipline engineering with courses in Smart Grid, Distributed Smart Energy Systems, Big Data Analytics, Energy Informatics.</p> <p>Secondary: Research scientists, academicians, computer and power engineers, power system operators, smart energy system planners.</p>

Autoren/Hrsg.

Kolhe, Mohan

Singh, Pushpendra

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie

Inhaltsverzeichnis

1. Big data analytics in distributed electrical energy system2. Data-driven applications for distributed electrical energy network topologies3. Machine learning techniques for load forecasting and their relative analysis4. Artificial intelligence techniques for modelling of power intensive load5. Data driven approaches for demand side management of power intensive loads with grid constraints6. Renewable energy prediction within distributed network7. Economic load dispatching through data-based computing techniques for distributed generators8. Electric vehicles charging stations coordination using predictive stochastic analysis9. Deregulated electrical energy pricing predictions for distributed electrical energy network operation10. Voltage security assessments in electrical energy network using power system operational data11. Smart device for power flow management within distributed network12. Communication of big data in smart grid13. Smart grid communication through cognitive radio using co-operative spectrum sensing

Über Autor(innen)

Kolhe, Mohan
Professor (Dr) Mohan Kolhe is with the University of Agder (Norway) as full professor in electrical power engineering with focus in smart grid and renewable energy. He has received the offer of professorship in smart grid from the Norwegian University of Science and Technology (NTNU). He has more than twenty-five years' academic experience at international level on electrical and renewable energy systems. He is a leading renewable energy technologist and has previously held academic positions at the world's prestigious universities e.g. University College London (UK / Australia), University of Dundee (UK); University of Jyvaskyla (Finland); and Hydrogen Research Institute, QC (Canada).

He was a member of the Government of South Australia's Renewable Energy Board (2009-2011) and worked on developing renewable energy policies.

Presently he is leading the EU FP7 Smart Grid-ICT project 'Scalable Energy Management Infrastructure for Household' as Technical Manager. This project is in collaboration with 12 EU partners from 4 EU countries.

His academic work ranges from the smart grid, grid integration of renewable energy systems, home energy management system, integrated renewable energy systems for hydrogen production, techno-economics of energy systems, solar and wind energy engineering, development of business models for distributed generation.

Singh, Pushpendra
Pushpendra Singh is Associate Professor in Electrical Engineering in the Institute of Engineering and Technology at JK Lakshmipat University, Jaipur, India.