Impact reduction potential by usage anticipation under comfort trade-off conditions

Duflou, Joost R.; Auquilla, Andres; Bock, Yannick De; Nowé, Ann; Kellens, Karel

doi:10.1016/j.cirp.2016.04.087

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…24h) predictions are straightforward with the unsupervised approach as it suces to identify the current prole. A wide variety of applications can benet from intelligent control systems, like the one presented in this paper, which can be illustrated by the framework developed in [11] that identies the reduction potential of any application by intelligent control.…”

Section: Methods and Applicationsmentioning

confidence: 99%

“…The threshold should therefore be set in an appropriate manner which is typically case specic. For example, in previous work [11], a user tolerance parameter U T was dened, binarising the user prole by discarding, i.e. setting to zero, U T % of the area under the curve with the lowest probabilities and elevating the remaining probabilities to 1.…”

Section: Predictive Performance Analysismentioning

confidence: 99%

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Nonparametric user activity modelling and prediction

Bock

Auquilla

Nowé

et al. 2020

User Model User-Adap Inter

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Modelling the occupancy of buildings, rooms or the usage of machines has many applications in varying elds, exemplied 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 non-parametric user activity modelling algorithm, i.e. a Dirichlet process mixture (DPM) 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 welldened 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.

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Section: Methods and Applicationsmentioning

confidence: 99%

Section: Predictive Performance Analysismentioning

confidence: 99%

Nonparametric user activity modelling and prediction

Bock

Auquilla

Nowé

et al. 2020

User Model User-Adap Inter

Self Cite

View full text Add to dashboard Cite

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“…A methodology to determine the reduction potential by intelligent control of any given application was developed by Duflou et al (2016), as part of the PERPETUAL project [169]. The authors identified the following selection of system and usage features that influence the reduction potential [ where ∆ 8/./, represents the difference between 8/./, and 8/.1=AB , 8/./, the duration of a transition from standby to a certain state (e.g.…”

Section: Intelligent Systemsmentioning

confidence: 99%

Design for reduced resource consumption during the use phase of products

et al. 2017

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Much work on sustainable design has focused on product manufacture/assembly and end of life. Gains in products' technical efficiency address the use phase, but how these products are used clearly affects resource consumption. There are two main approaches to design interventions to reduce resource consumption during product life. Firstly, interventions aim to change user behavior, through information and feedback, as well as physical product affordances abstracted from lead users to guide or steer users toward the desired behavior. Secondly, automatic adjustment of product systems performance levels based on personal user profiles and anticipated usage is implemented using artificial intelligence techniques.

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