Identifying important state variables for a blind controller

Daum, David; Morel, Nicolas

doi:10.1016/j.buildenv.2009.09.009

Cited by 21 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The aforementioned goals were achieved by preliminary studies that identify the important input variables for a control [30], assess the maximal saving potential of a blind control [2], and compare the energetic footprint of an optimal control to user behavior and simple on/off schemes [15]. The final control is based on a non-linear model predictive control (NMPC).…”

Section: The Control Algorithmmentioning

confidence: 99%

A personalized measure of thermal comfort for building controls

Daum

Haldi

Morel

2011

Building and Environment

Self Cite

166

View full text Add to dashboard Cite

Section: The Control Algorithmmentioning

confidence: 99%

A personalized measure of thermal comfort for building controls

Daum

Haldi

Morel

2011

Building and Environment

Self Cite

166

View full text Add to dashboard Cite

“…The focus of this paper is the study of human interactions with window shades and electric lights. Previous studies with similar focus, have been designed around two main objectives: First, to attain an understanding of the reasoning behind the human-building interactions towards the development of adaptive control algorithms (Guillemin and Molteni, 2002;Guillemin and Morel, 2001; Daum and Morel, 2010;Lindelof 2009;Gunay et al, 2014). Second, to develop stochastic models based on probabilistic relationships between human-shading interactions and environmental conditions that represent the random nature of occupant behavior (Haldi and Robinson, 2010;da Silva et al, 2013;Inkarojrit, 2008;Reinhart, 2004), and when used properly, achieve more reliable predictions in Building Performance Simulation (BPS) (Yan et al, 2015;da silva et al, 2014;Parys et al, 2011;Gunay et al, 2015;Gaetani et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Bayesian modeling approach of human interactions with shading and electric lighting systems in private offices

Sadeghi

Awalgaonkar

Karava

et al. 2017

Energy and Buildings

View full text Add to dashboard Cite

In this paper, we present a hierarchical Bayesian approach to model human interactions with motorized roller shades and dimmable electric lights. At the top level of hierarchy, Bayesian multivariate binarychoice logit models predict the probability of shade raising/lowering actions as well as the actions to increase the level of electric light. At the bottom level, Bayesian regression models with built-in physical constraints estimate the magnitude of actions, and hence the corresponding operating states of shading and electric lighting systems. The models are based on a dataset from a field study conducted in private offices designed to facilitate a large number of participants and to collect data on environmental parameters as well as individual characteristics and human attributes governing human-shading andelectric lighting interactions. The methodology presented in our study demonstrates the advantages of the Bayesian approach that captures the epistemic uncertainty in the model parameters, which is important when dealing with small-sized datasets, a ubiquitous issue in human data collection in actual buildings; it also enables the incorporation of prior beliefs about the systems; and offers a systematic way to select amongst different models using the Bayes factor and the evidence for each model. This paper presents models developed only for arrival periods due to the low frequency of actions during intermediate time intervals with continuous occupation. Our findings reveal that besides environmental variables, human attributes are significant predictors of human interactions, and improve the predictive performance when incorporated as features in shading action models.

show abstract

“…This is a well-known evolutionary algorithm, which has been successfully applied to a number of building optimization problems (Daum and Morel 2010;Sanaye and Hajabdollahi, 2010;Chantrelle et al, 2011;Jin et al, 2011;.…”

Section: The Optimization Algorithmmentioning

confidence: 99%