Algorithms
Learning in the presence of concept drift and hidden contexts
Machine Learning
Processing forecasting queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Indexing forecast models for matching and maintenance
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
XML and knowledge based process model reuse and management in business intelligence system
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
Data management in the MIRABEL smart grid system
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Efficient integration of external information into forecast models from the energy domain
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
pEDM: online-forecasting for smart energy analytics
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Continuous balancing of energy demand and supply is a fundamental prerequisite for the stability and efficiency of energy grids. This balancing task requires accurate forecasts of future electricity consumption and production at any point in time. For this purpose, database systems need to be able to rapidly process forecasting queries and to provide accurate results in short time frames. However, time series from the electricity domain pose the challenge that measurements are constantly appended to the time series. Using a naive maintenance approach for such evolving time series would mean a re-estimation of the employed mathematical forecast model from scratch for each new measurement, which is very time consuming. We speed-up the forecast model maintenance by exploiting the particularities of electricity time series to reuse previously employed forecast models and their parameter combinations. These parameter combinations and information about the context in which they were valid are stored in a repository. We compare the current context with contexts from the repository to retrieve parameter combinations that were valid in similar contexts as starting points for further optimization. An evaluation shows that our approach improves the maintenance process especially for complex models by providing more accurate forecasts in less time than comparable estimation methods.