Proceedings ArticleDOI
Building an occupancy model from sensor networks in office environments
TLDR
The work presented here aims to answer the question: Using just binary occupancy sensors is it possible to build a behaviour occupancy model over long-term logged data and shows the power of the LDA model in extracting relevant patterns from sensor network data.Abstract:
The work presented here aims to answer this question: Using just binary occupancy sensors is it possible to build a behaviour occupancy model over long-term logged data? Sensor measurements are grouped to form artificial words (activities) and documents (set of activities). The goal is to infer the latent topics which are assumed to be the common routines from the observed data. An unsupervised probabilistic model, namely the Latent Dirichlet Allocation (LDA), is applied to automatically discover the latent topics (routines) in the data. Experimental results using real logged data of 24 weeks from an office building, with different number of topics, are shown. The results show the power of the LDA model in extracting relevant patterns from sensor network data.read more
Citations
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Journal ArticleDOI
Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models
TL;DR: Interestingly, using only one predictor (temperature) the LDA model was able to estimate the occupancy with accuracies of 85% and 83% in the two testing sets.
Journal ArticleDOI
Building occupancy estimation and detection: A review
TL;DR: A comprehensive review on building occupancy estimation and detection is presented and some potential future research directions are indicated based on current progresses of the systems.
Journal ArticleDOI
Review on occupancy detection and prediction in building simulation
TL;DR: This review provides theoretical guidance for building design and makes contributions to building energy conservation and thermal comfort through the implementation of intelligent control strategies based on occupancy monitoring and prediction.
Journal ArticleDOI
RUP: Large Room Utilisation Prediction with carbon dioxide sensor
TL;DR: This work presents large Room Utilisation Prediction with carbon dioxide sensor (RUP), a novel way to estimate the number of people within a closed space from a single carbon dioxide Sensor, which de-noises and pre-processes the carbon dioxide and indoor human occupancy data.
Journal ArticleDOI
Learning routines over long-term sensor data using topic models
TL;DR: In this work, topic models are employed to learn the latent structure and dynamics of sensor network data and have shown the ability to find routines of activity over sensor networkData in office environments.
References
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Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Journal ArticleDOI
Finding scientific topics
TL;DR: A generative model for documents is described, introduced by Blei, Ng, and Jordan, and a Markov chain Monte Carlo algorithm is presented for inference in this model, which is used to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics.
Journal ArticleDOI
An introduction to variational methods for graphical models
TL;DR: This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields), and describes a general framework for generating variational transformations based on convex duality.
Proceedings ArticleDOI
The author-topic model for authors and documents
TL;DR: The author-topic model is introduced, a generative model for documents that extends Latent Dirichlet Allocation to include authorship information, and applications to computing similarity between authors and entropy of author output are demonstrated.