Proceedings of the Joint EDBT/ICDT 2013 Workshops 2013
DOI: 10.1145/2457317.2457361
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Towards the automated extraction of flexibilities from electricity time series

Abstract: Several recent and ongoing smart grid projects aim at incorporating more renewable energy sources (RES) into the energy production. Among them, the European MIRABEL project tackles this problem by managing flexibilities on energy demand and supply. Typically, this project assumes that some parts of the energy demand can be shifted when the RES production is sufficient, e.g., the washing machine can be turned on when the wind blows. To express these flexibilities, the project introduces the core-concept of flex… Show more

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Cited by 5 publications
(3 citation statements)
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“…The number of the flex-offers is similar to the one of the previous dataset. Furthermore, historical consumption time series of customers and the flex-offer generator tool described in [7] were used to create the datasets. The flexoffer generator tool was used to generate both positive and negative flex-offers.…”
Section: Methodsmentioning
confidence: 99%
“…The number of the flex-offers is similar to the one of the previous dataset. Furthermore, historical consumption time series of customers and the flex-offer generator tool described in [7] were used to create the datasets. The flexoffer generator tool was used to generate both positive and negative flex-offers.…”
Section: Methodsmentioning
confidence: 99%
“…ML-based techniques: Machine Learning (ML) based FOs generation relies on user behavior data and predictive models for estimating various FO parameters, which reduces or altogether eliminates user interaction [18,27]. The FO generation process starts with the gathering of the energy demand time series and available context information relevant to the flexible load.…”
Section: Flexibility Extraction and Management 41 Flexibility Extracmentioning
confidence: 99%
“…The number of the flex-objects is similar to the one of the previous dataset. Furthermore, historical consumption time series of customers and the flex-object generator tool described in [41] were used to create the datasets. The flex-object generator tool was used to generate both positive and negative flex-objects.…”
Section: Methodsmentioning
confidence: 99%