Browsing by Author "De Bock, Yannick"

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    Nonparametric user activity modelling and prediction
    (2020) Nowé, Ann; De Bock, Yannick; Duflou, Joost R; Auquilla Sangolquí, Andrés Vinicio
    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 , of multiple users. Furthermore, it can also be used for modelling and predicting appliance usage (e.g. ). 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.
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    The energy saving potential of retrofitting a smart heating system: a residence hall pilot study
    (2021) Duflou, Joost R.; De Bock, Yannick; Auquilla Sangolquí, Andrés Vinicio; Bracquené, Ellen; Nowé, Ann
    Energy conservation is of increasing importance in contemporary society. A large fraction of energy end-use can be attributed to space conditioning. Therefore, intelligent control systems were devised and commercialised in the form of smart thermostats. Hereto, the availability of occupancy information is essential such that heating and/or cooling schedules can be tailored to user needs. This way energy savings can be obtained without jeopardising user satisfaction. However, preceding studies generally rely on simulations to estimate the potential reduction in energy consumption. This work aims at quantifying the potential based on a real life experiment. The development of a smart heating system is presented along with the results of an actual field test of retrofitting this system in 14 single-user student rooms of a university residence hall. An experiment was conducted in which the heating was automatically steered for 1 week (26 March 2018–01 April 2018). Total energy savings range between 26.9% and 59.5% and calculated thermal comfort was not significantly affected by the autonomous control. Furthermore, an environmental impact reduction of 3.2 to 12.9 EcoPoints is estimated for the controlled week, resulting in a reduction of 37.5 to 150.2