Real-Time Business Intelligence in the MIRABEL Smart Grid System

The so-called smart grid is emerging in the energy domain as a solution to provide a stable, efficient and sustainable energy supply accommodating ever growing amounts of renewable energy like wind and solar in the energy production. Smart grid systems are highly distributed, manage large amounts of energy related data, and must be able to react rapidly (but intelligently) when conditions change, leading to substantial real-time business intelligence challenges. This paper discusses these challenges and presents data management solutions in the European smart grid project MIRABEL. These solutions include real-time time series forecasting, real-time aggregation of the flexibilities in energy supply and demand, managing subscriptions for forecasted and flexibility data, efficient storage of time series and flexibilities, and real-time analytical query processing spanning past and future (forecasted) data. Experimental studies show that the proposed solutions support important real-time business intelligence tasks in a smart grid system.

[1]  Torben Bach Pedersen,et al.  Model-based Integration of Past & Future in TimeTravel , 2012, Proc. VLDB Endow..

[2]  Wolfgang Lehner,et al.  Evaluation of Load Scheduling Strategies for Real-Time Data Warehouse Environments , 2009, BIRTE.

[3]  Torben Bach Pedersen,et al.  RiTE: Providing On-Demand Data for Right-Time Data Warehousing , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[4]  Wolfgang Lehner,et al.  Partitioning and Multi-core Parallelization of Multi-equation Forecast Models , 2012, SSDBM.

[5]  Yanlei Diao,et al.  High-performance complex event processing over streams , 2006, SIGMOD Conference.

[6]  Torben Bach Pedersen,et al.  Data management in the MIRABEL smart grid system , 2012, EDBT-ICDT '12.

[7]  Clive W. J. Granger,et al.  Short-run forecasts of electricity loads and peaks , 2001 .

[8]  Wolfgang Lehner,et al.  Context-Aware Parameter Estimation for Forecast Models in the Energy Domain , 2011, SSDBM.

[9]  James W. Taylor,et al.  Triple seasonal methods for short-term electricity demand forecasting , 2010, Eur. J. Oper. Res..

[10]  Michael Stonebraker,et al.  Aurora: a data stream management system , 2003, SIGMOD '03.

[11]  Wolfgang Lehner,et al.  Forcasting Evolving Time Series of Energy Demand and Supply , 2011, ADBIS.

[12]  Alfons Kemper,et al.  HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[13]  Hagit Shatkay,et al.  Approximate queries and representations for large data sequences , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[14]  Wolfgang Lehner,et al.  Offline Design Tuning for Hierarchies of Forecast Models , 2011, BTW.

[15]  Samuel Madden,et al.  Querying continuous functions in a database system , 2008, SIGMOD Conference.

[16]  Torben Bach Pedersen,et al.  Using Aggregation to Improve the Scheduling of Flexible Energy Offers , 2012 .

[17]  Torben Bach Pedersen,et al.  Optimizing Notifications of Subscription-Based Forecast Queries , 2012, SSDBM.

[18]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .