Abstract
Good demand side management in smart grids does not only depend on the amount of energy consumed by various appliances, but also on the temporal characteristics of the consumption, i.e. the load profile of the appliances. Representative load profiles can be used for predicting future energy consumption. However, a load profile is hard to characterise as it often depends on the operational conditions of the appliance when the measurements were taken. For instance the load profile of a washing machine will depend on the amount of cloths and the inlet water temperature. This paper presents a methodology for empirically obtaining the load profile from an ensemble of event driven traces of a stochastically varying mode of an appliance.
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant number 608806 CoSSMic.
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Horn, G., Venticinque, S., Amato, A. (2015). Inferring Appliance Load Profiles from Measurements. In: Di Fatta, G., Fortino, G., Li, W., Pathan, M., Stahl, F., Guerrieri, A. (eds) Internet and Distributed Computing Systems. IDCS 2015. Lecture Notes in Computer Science(), vol 9258. Springer, Cham. https://doi.org/10.1007/978-3-319-23237-9_11
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