157 research outputs found
Sequential Logistic Principal Component Analysis (SLPCA): Dimensional Reduction in Streaming Multivariate Binary-State System
Sequential or online dimensional reduction is of interests due to the
explosion of streaming data based applications and the requirement of adaptive
statistical modeling, in many emerging fields, such as the modeling of energy
end-use profile. Principal Component Analysis (PCA), is the classical way of
dimensional reduction. However, traditional Singular Value Decomposition (SVD)
based PCA fails to model data which largely deviates from Gaussian
distribution. The Bregman Divergence was recently introduced to achieve a
generalized PCA framework. If the random variable under dimensional reduction
follows Bernoulli distribution, which occurs in many emerging fields, the
generalized PCA is called Logistic PCA (LPCA). In this paper, we extend the
batch LPCA to a sequential version (i.e. SLPCA), based on the sequential convex
optimization theory. The convergence property of this algorithm is discussed
compared to the batch version of LPCA (i.e. BLPCA), as well as its performance
in reducing the dimension for multivariate binary-state systems. Its
application in building energy end-use profile modeling is also investigated.Comment: 6 pages, 4 figures, conference submissio
PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring
Non-intrusive presence detection of individuals in commercial buildings is
much easier to implement than intrusive methods such as passive infrared,
acoustic sensors, and camera. Individual power consumption, while providing
useful feedback and motivation for energy saving, can be used as a valuable
source for presence detection. We conduct pilot experiments in an office
setting to collect individual presence data by ultrasonic sensors, acceleration
sensors, and WiFi access points, in addition to the individual power monitoring
data. PresenceSense (PS), a semi-supervised learning algorithm based on power
measurement that trains itself with only unlabeled data, is proposed, analyzed
and evaluated in the study. Without any labeling efforts, which are usually
tedious and time consuming, PresenceSense outperforms popular models whose
parameters are optimized over a large training set. The results are interpreted
and potential applications of PresenceSense on other data sources are
discussed. The significance of this study attaches to space security, occupancy
behavior modeling, and energy saving of plug loads.Comment: BuildSys 201
Social Game for Building Energy Efficiency: Utility Learning, Simulation, and Analysis
We describe a social game that we designed for encouraging energy efficient
behavior amongst building occupants with the aim of reducing overall energy
consumption in the building. Occupants vote for their desired lighting level
and win points which are used in a lottery based on how far their vote is from
the maximum setting. We assume that the occupants are utility maximizers and
that their utility functions capture the tradeoff between winning points and
their comfort level. We model the occupants as non-cooperative agents in a
continuous game and we characterize their play using the Nash equilibrium
concept. Using occupant voting data, we parameterize their utility functions
and use a convex optimization problem to estimate the parameters. We simulate
the game defined by the estimated utility functions and show that the estimated
model for occupant behavior is a good predictor of their actual behavior. In
addition, we show that due to the social game, there is a significant reduction
in energy consumption
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