Big Data Analysis for Smart Electrical Energy Distribution Systems

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. Presents a systematic and integrated reference on data-driven applications for solving the problems of electrical energy distribution network topologies using smart energy meter data Provides a comprehensive look at the machine learning techniques available for renewable energy prediction, demand forecasting, and intelligent techno-economic operation and control of distributed energy systems Features specific data driven approaches for demand side management with grid constraints and the development of dynamic electrical energy pricing INDICE: 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

• ISBN: 978-0-323-85556-3
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 310
• Fecha Publicación: 01/04/2022
• Nº Volúmenes: 1
• Idioma: Inglés