Abstract
Occupancy is used to represent the movements and locations of users among various zones of buildings, and it is the basis of all other daily energy consumption behaviors. This study investigated eight families in cold areas of China based on occupancy measurements obtained in four main rooms, i.e., living room, bedroom, kitchen, and bathroom. In particular, we analyzed the duration of user occupancy and hourly mean occupancy, and characterized their regular and random features. According to the results, we developed an event-based occupancy model using an inhomogeneous Markov chain, where the rooms were modeled and daily events were divided into three categories according to their randomness. We established a new method for conversion between event characteristic parameters and a transition probability matrix, as well as an overlap avoidance method for active events. The model was then validated using real data. The results showed that the model performed well in terms of two evaluation criteria. The model should improve the accuracy of simulations of occupancy.
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References
Aerts D, Minnen J, Glorieux I, et al. (2014). A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison. Building and Environment, 75: 67–78.
Blight TS, Coley DA (2013). Sensitivity analysis of the effect of occupant behaviour on the energy consumption of passive house dwellings. Energy and Buildings, 66: 183–192.
Chang WK, Hong T (2013). Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data. Building Simulation, 6: 23–32.
Chen Z, Xu J, Soh YC (2015). Modeling regular occupancy in commercial buildings using stochastic models. Energy and Buildings, 103: 216–223.
Cover TM, Thomas JA (2006). Elements of information theory, 2nd edn. New York: John Wiley & Sons.
Crawley DB, Lawrie LK, Winkelmann FC, et al. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.
Diao L, Sun Y, Chen Z, et al. (2017). Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation. Energy and Buildings, 147: 47–66.
Dong B, Lam KP, Neuman CP (2011). Integrated building control based on occupant behavior pattern detection and local weather forecasting. In: Proceedings of the 12th International IBPSA Building Simulation Conference, Sydney, Australia.
Fajilla G, Austin MC, Mora D, et al. (2021). Assessment of probabilistic models to estimate the occupancy state in office buildings using indoor parameters and user-related variables. Energy and Buildings, 246: 111105.
Flett G, Kelly N (2016). An occupant-differentiated, higher-order Markov Chain method for prediction of domestic occupancy. Energy and Buildings, 125: 219–230.
Flett G, Kelly N (2021). Modelling of individual domestic occupancy and energy demand behaviours using existing datasets and probabilistic modelling methods. Energy and Buildings, 252: 111373.
Hong T, Taylor-Lange SC, D’Oca S, et al. (2016). Advances in research and applications of energy-related occupant behavior in buildings. Energy and Buildings, 116: 694–702.
Jeong B, Kim J, de Dear R (2021). Creating household occupancy and energy behavioural profiles using national time use survey data. Energy and Buildings, 252: 111440.
Jiang Y, Hu S (2021). Paths to carbon neutrality in China’s building sector. Journal of HV&AC, 51(5): 1–13. (in Chinese)
Li Y, Yamaguchi Y, Shimoda Y (2022). Impact of the pre-simulation process of occupant behaviour modelling for residential energy demand simulations. Journal of Building Performance Simulation, 15: 287–306.
Liao C, Lin Y, Barooah P (2012). Agent-based and graphical modelling of building occupancy. Journal of Building Performance Simulation, 5: 5–25.
Malekpour Koupaei DM, Cetin KS, Passe U (2022). Stochastic residential occupancy schedules based on the American Time-Use Survey. Science and Technology for the Built Environment, 28: 776–790.
McKenna E, Krawczynski M, Thomson M (2015). Four-state domestic building occupancy model for energy demand simulations. Energy and Buildings, 96: 30–39.
Mitra D, Chu Y, Cetin K (2021). Cluster analysis of occupancy schedules in residential buildings in the United States. Energy and Buildings, 236: 110791.
Page J, Robinson D, Morel N, et al. (2008). A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 40: 83–98.
Ren XX, Yan D (2014). A study of lighting energy consumption model for office buildings based on occupant behavior. Building Science, 30(6): 1–9. (in Chinese)
Richardson I, Thomson M, Infield D (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40: 1560–1566.
Rueda L, Sansregret S, Le Lostec B, et al. (2021). A probabilistic model to predict household occupancy profiles for home energy management applications. IEEE Access, 9: 38187–38201.
Salimi S, Liu Z, Hammad A (2019). Occupancy prediction model for open-plan offices using real-time location system and inhomogeneous Markov chain. Building and Environment, 152: 1–16.
Serfozo R (2009). Basics of Applied Stochastic Processes. Heidelberg, Germany: Springer.
Sun K, Yan D, Hong T, et al. (2014). Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration. Building and Environment, 79: 1–12.
Wang D, Federspiel CC, Rubinstein F (2005). Modeling occupancy in single person offices. Energy and Buildings, 37: 121–126.
Wang C, Yan D, Jiang Y (2011). A novel approach for building occupancy simulation. Building Simulation, 4: 149–167.
Wang C (2014). Simulation research on occupant energy-related behaviors in building. PhD Thesis, Tsinghua University, China. (in Chinese)
Wang C, Yan D, Feng X, et al. (2015). A Markov chain and event based model for building occupant movement process. Building Science, 31(10): 188–198. (in Chinese)
Wang Y, Gu BH (2021). New process of China’s sustainable development: Exploring the road towards carbon neutrality. China Sustainability Tribune, 2021(6): 15–20. (in Chinese)
Wilke U, Haldi F, Scartezzini JL, et al. (2013). A bottom-up stochastic model to predict building occupants’ time-dependent activities. Building and Environment, 60: 254–264.
Yamaguchi Y, Shimoda Y (2017). A stochastic model to predict occupants’ activities at home for community-/urban-scale energy demand modelling. Journal of Building Performance Simulation, 10: 565–581.
Yan D, O’Brien W, Hong T, et al. (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107: 264–278.
Yao R, Steemers K (2005). A method of formulating energy load profile for domestic buildings in the UK. Energy and Buildings, 37: 663–671.
Zhang Y, Bai X, Mills FP, et al. (2018). Rethinking the role of occupant behavior in building energy performance: A review. Energy and Buildings, 172: 279–294.
Zhang L, Huang X, Chen YY (2019). Literature review on residential energy behavior. Urbanism and Architecture, 16(2): 160–164. (in Chinese)
Acknowledgements
The authors gratefully acknowledge the funding support from the National Natural Science Foundation of China (No. 52008129), the Postdoctoral Science Foundation of China (No. 2019M651289), and the National Natural Science Foundation of Heilongjiang Province (No. LH2020E051, No. GZ20210211).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Qi Dong and Zikai Ma. The first draft of the manuscript was written by Zikai Ma and all authors commented on previous versions of the manuscript. Cheng Sun guided and revised the manuscript. All authors read and approved the final manuscript.
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The authors have no competing interests to declare that are relevant to the content of this article.
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Dong, Q., Ma, Z. & Sun, C. Occupancy of rooms in urban residential buildings by users in cold areas of China. Build. Simul. 16, 483–497 (2023). https://doi.org/10.1007/s12273-022-0950-8
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DOI: https://doi.org/10.1007/s12273-022-0950-8