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- Title
Nonparametric user activity modelling and prediction.
- Authors
De Bock, Yannick; Auquilla, Andres; Nowé, Ann; Duflou, Joost R.
- Abstract
Modelling the occupancy of buildings, rooms or the usage of machines has many applications in varying fields, exemplified by the fairly recent emergence of smart, self-learning thermostats. Typically, the aim of such systems is to provide insight into user behaviour and incentivise energy savings or to automatically reduce consumption while maintaining user comfort. This paper presents a nonparametric user activity modelling algorithm, i.e. a Dirichlet process mixture model implemented by Gibbs sampling and the stick-breaking process, to infer the underlying patterns in user behaviour from the data. The technique deals with multiple activities, such as <present, absent, sleeping>, of multiple users. Furthermore, it can also be used for modelling and predicting appliance usage (e.g. <on, standby, off>). The algorithm is evaluated, both on cluster validity and predictive performance, using three case studies of varying complexity. The obtained results indicate that the method is able to properly assign the activity data into well-defined clusters. Moreover, the high prediction accuracy demonstrates that these clusters can be exploited to anticipate future behaviour, facilitating the development of intelligent building management systems.
- Subjects
PREDICTION models; THERMOSTAT; BUILDING operation management; PREDICTIVE validity; FORECASTING; ALGORITHMS; GIBBS sampling; INTELLIGENT buildings
- Publication
User Modeling & User-Adapted Interaction, 2020, Vol 30, Issue 5, p803
- ISSN
0924-1868
- Publication type
Article
- DOI
10.1007/s11257-020-09259-3