Name
Abstract | ||
|---|---|---|
The eective management of a power grid with an increasing share of (distributed) renewables and more and more available data, e.g., coming from smart meters, heavily relies on advanced data analytics such as demand and supply forecasting. In this context, data management is one major challenge in electric grids. Large amount of data from multiple heterogeneous sources require transformations, e.g., spatio-temporal alignment or anomaly detection, to serve data analytics tasks and are often applied on dierent views of the data, e.g., on state, substation or feeder level. In this paper, the progress on the development of an energy data management systems for the electricity grid is presented. The design of the system was inspired by the realworld use case of forecasting short-term energy demand in Vermont, using data from a combination of SCADA, smart meters and weather forecasting services. A general data model addressing the aforementioned challenges and aimed at supporting advanced data analytics is introduced. The proposed data model views a time series as an abstract concept that might represent raw measurements or arbitrary operations. The benets of the system is demonstrated for the design and live update energy demand forecasts. |
Year | Venue | Field |
|---|---|---|
2016 | EDBT/ICDT Workshops | Data science,Anomaly detection,Demand forecasting,Data analysis,Industrial engineering,Computer science,SCADA,Analytics,Weather forecasting,Data management,Data model |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Fusco | 1 | 147 | 13.48 |
| Ulrike Fischer | 2 | 108 | 12.04 |
| Vincent Lonij | 3 | 1 | 1.09 |
| Pascal Pompey | 4 | 22 | 2.40 |
| Jean-Baptiste Fiot | 5 | 10 | 1.98 |
| Bei Chen | 6 | 26 | 9.11 |
| Yiannis Gkoufas | 7 | 17 | 5.96 |
| Mathieu Sinn | 8 | 10 | 3.65 |