Abstract
The increasing drive towards green energy has boosted the installation of Renewable Energy Sources (RES). Increasing the share of RES in the power grid requires demand management by flexibility in the consumption. In this paper, we perform a state-of-the-art analysis on the flexibility and operation patterns of the devices in a set of real households. We propose a number of specific pre-processing steps such as operation stage segmentation, and aberrant operation duration removal to clean device level data. Further, we demonstrate various device operation properties such as hourly and daily regularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for load- and flexibility-detection and -prediction at the device level.
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Notes
- 1.
More precisely, the expected frequency, as described in Sect. 5.4.
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Acknowledgment
This work was supported in part by the TotalFlex project sponsored by the ForskEL program of Energinet.dk.
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© 2014 Springer International Publishing Switzerland
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Neupane, B., Pedersen, T.B., Thiesson, B. (2014). Towards Flexibility Detection in Device-Level Energy Consumption. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2014. Lecture Notes in Computer Science(), vol 8817. Springer, Cham. https://doi.org/10.1007/978-3-319-13290-7_1
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DOI: https://doi.org/10.1007/978-3-319-13290-7_1
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