A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Processing forecasting queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
MAD skills: new analysis practices for big data
Proceedings of the VLDB Endowment
Overview of sciDB: large scale array storage, processing and analysis
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Ricardo: integrating R and Hadoop
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Context-aware parameter estimation for forecast models in the energy domain
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Forcasting evolving time series of energy demand and supply
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
F2DB: The Flash-Forward Database System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Data management in the MIRABEL smart grid system
Proceedings of the 2012 Joint EDBT/ICDT Workshops
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Continuous balancing of energy demand and supply is a fundamental prerequisite for the stability of energy grids and requires accurate forecasts of electricity consumption and production at any point in time. Today's Energy Data Management (EDM) systems already provide accurate predictions, but typically employ a very time-consuming and inflexible forecasting process. However, emerging trends such as intra-day trading and an increasing share of renewable energy sources need a higher forecasting efficiency. Additionally, the wide variety of applications in the energy domain pose different requirements with respect to runtime and accuracy and thus, require flexible control of the forecasting process. To solve this issue, we introduce our novel online forecasting process as part of our EDM system called pEDM. The online forecasting process rapidly provides forecasting results and iteratively refines them over time. Thus, we avoid long calculation times and allow applications to adapt the process to their needs. Our evaluation shows that our online forecasting process offers a very efficient and flexible way of providing forecasts to the requesting applications.