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Título : | Nonparametric user activity modelling and prediction |
Autor: | Auquilla Sangolqui, Andres Vinicio De Bock, Yannick Duflou, Joost R Nowé, Ann |
Correspondencia: | De Bock, Yannick, yannick.debock@kuleuven.be |
Palabras clave : | Occupancy prediction Activity recognition Clustering Dirichlet process mixture |
Área de conocimiento FRASCATI amplio: | 2. Ingeniería y Tecnología |
Área de conocimiento FRASCATI detallado: | 2.1.3 Ingeniería en Construcción |
Área de conocimiento FRASCATI específico: | 2.1 Ingeniería Civil |
Área de conocimiento UNESCO amplio: | 07 - Ingeniería, Industria y Construcción |
ÁArea de conocimiento UNESCO detallado: | 0732 - Construcción e Ingeniería Civil |
Área de conocimiento UNESCO específico: | 073 - Arquitectura y Construcción |
Fecha de publicación : | 2020 |
Fecha de fin de embargo: | 12-jun-2050 |
Volumen: | Volumen 0 |
Fuente: | User Modeling and User-Adapted Interaction |
metadata.dc.identifier.doi: | 10.1007/s11257-020-09259-3 |
Tipo: | ARTÍCULO |
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. © 2020, Springer Nature B.V. |
Resumen : | 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. © 2020, Springer Nature B.V. |
URI : | http://dspace.ucuenca.edu.ec/handle/123456789/34497 https://link.springer.com/article/10.1007%2Fs11257-020-09259-3 |
URI Fuente: | https://www.springer.com/journal/11257 |
ISSN : | 09241868 |
Aparece en las colecciones: | Artículos
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