Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings

Title: Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings

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Abstract

In this paper, we present a simple yet effective algorithm to automatically assign reference temperature set-points based on the occupancy information. Both the binary and detailed occupancy estimation cases are considered. In the first case study, we assume the schedule involves only binary states (occupied or not occupied), i.e. the room is invariant. With long-term observations occupancy levels can be estimated using statistical tools. In the second case study, two techniques are introduced. Firstly, we propose an identification-based approaches. More precisely, we identify the models via Expectation Maximization (EM) approach. The statistical state space model is built in linear form for the mapping between the occupancy measurements and real occupancy states with noise considered. Secondly, we introduce a novel finite state automata (FSA) which is successfully reconstructed by general systems problem solver (GSPS). As far as we know, no studies have used the finite state machine or general system theory to estimate occupancy in buildings. All above estimates can be used to adaptively update the temperature set-points for Heating, Ventilation, and Air-Conditioning (HVAC) control strategy.

Authors:

Djouadi, Seddik M ^[1]; Nutaro, James J ^[1]; Kuruganti, Teja ^[1]

ORNL

Publication Date:: 2016-01-01

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Building Technologies Research and Integration Center (BTRIC)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

OSTI Identifier:: 1319200

DOE Contract Number:: AC05-00OR22725

Resource Type:: Conference

Resource Relation:: Conference: 4th International High Performance Buildings Conference at Purdue, West Lafayette, IN, USA, 20160711, 20160711

Country of Publication:: United States

Language:: English

Citation Formats


                    Djouadi, Seddik M, Nutaro, James J, and Kuruganti, Teja. Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings.  United States: N. p., 2016. 
        Web.

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                    Djouadi, Seddik M, Nutaro, James J, & Kuruganti, Teja. Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings.  United States.

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                    Djouadi, Seddik M, Nutaro, James J, and Kuruganti, Teja. Fri .  
        "Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings".  United States.

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@article{osti_1319200,

  title        = {Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings},

  author       = {Djouadi, Seddik M and Nutaro, James J and Kuruganti, Teja},

  abstractNote = {In this paper, we present a simple yet effective algorithm to automatically assign reference temperature set-points based on the occupancy information. Both the binary and detailed occupancy estimation cases are considered. In the first case study, we assume the schedule involves only binary states (occupied or not occupied), i.e. the room is invariant. With long-term observations occupancy levels can be estimated using statistical tools. In the second case study, two techniques are introduced. Firstly, we propose an identification-based approaches. More precisely, we identify the models via Expectation Maximization (EM) approach. The statistical state space model is built in linear form for the mapping between the occupancy measurements and real occupancy states with noise considered. Secondly, we introduce a novel finite state automata (FSA) which is successfully reconstructed by general systems problem solver (GSPS). As far as we know, no studies have used the finite state machine or general system theory to estimate occupancy in buildings. All above estimates can be used to adaptively update the temperature set-points for Heating, Ventilation, and Air-Conditioning (HVAC) control strategy.},

  doi          = {},

  url          = {https://www.osti.gov/biblio/1319200},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2016},

  month        = {1}

}

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