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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.
Sommario
1. Big data analytics in distributed electrical energy system
2. Data-driven applications for distributed electrical energy network topologies
3. Machine learning techniques for load forecasting and their relative analysis
4. Artificial intelligence techniques for modelling of power intensive load
5. Data driven approaches for demand side management of power intensive loads with grid constraints
6. Renewable energy prediction within distributed network
7. Economic load dispatching through data-based computing techniques for distributed generators
8. Electric vehicles charging stations coordination using predictive stochastic analysis
9. Deregulated electrical energy pricing predictions for distributed electrical energy network operation
10. Voltage security assessments in electrical energy network using power system operational data
11. Smart device for power flow management within distributed network
12. Communication of big data in smart grid
13. Smart grid communication through cognitive radio using co-operative spectrum sensing
