| Peer-Reviewed

Analysis of Occupants’ Air-Conditioning Opening Behaviour Based on the Survival Model

Received: 26 October 2021    Accepted: 23 November 2021    Published: 24 November 2021
Views:       Downloads:
Abstract

It is essential to predict building energy consumption through more accurate simulation of building energy consumption, and then put forward suggestions for building energy conservation. Therefore, it is a very important issue to study the variable, random and complex air conditioning usage mode. In the previous studies on air conditioning, it could be found that whether the air conditioning is on or off, it is only a mathematical function about environmental parameters. However, when we arrive at the office and feel uncomfortable, we don't open the window immediately. Instead, we put up with it for a while. In view of the above shortcomings, this study proposed a survival model based on Weibull function to predict the air-conditioning on behavior. Through the verification of the model, we found that the accuracy of the air-conditioning regulation model based on the survival model is more than 74%. We compared and verified the common three-parameter Weibull model with the survival model-based Weibull model, and found that the accuracy of the common three-parameter Weibull model was slightly higher than that of the survival model. At the same time, we analyzed the death event (tolerance temperature) of the survival model, and further improving the tolerance temperature is of great help to the accuracy of the model.

Published in Urban and Regional Planning (Volume 6, Issue 4)
DOI 10.11648/j.urp.20210604.16
Page(s) 147-154
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Air-Conditioning Status, Air-Conditioning Opening Probability, Survival Models, Prediction Accuracy

References
[1] Y. Zhang, Da. Yan, S. Hu, S. Guo, Modelling of energy consumption and carbon emission from the building construction sector in China, a process-based LCA approach, Energy Policy 134 (2019) 110949, https://doi.org/10.1016/j.enpol.2019.110949.
[2] S. Hu, Da. Yan, S. Guo, Y. Cui, B. Dong, A survey on energy consumption and energy usage behavior of households and residential building in urban China, Energy Build. 148 (2017) 366–378.
[3] H. Sha, P. Xu, C. Hu, et al., A simplified HVAC energy prediction method based on degree-day, Sustain. Cities Soc. 51 (2019) 101698. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp. 68–73.
[4] X Ren, Da. Yan, C Wang, Air-conditioning usage conditional probability model for residential buildings, Building and Environment, 81 (2014) 172-182.
[5] Y. Peng, Z. Nagy, A. Schluter, Temperature-preference learning with neural networks for occupant-centric building indoor climate controls, Build. Environ. 154 (MAY) (2019) 296–308.
[6] J. Tanimoto, A. Hagishima, State transition probability for the Markov Model dealing with on/off cooling schedule in dwellings, Energy Build. 37 (3) (2005) 181–187.
[7] S. H. Mun, Y. Kwak, J. H. Huh, A case-centered behavior analysis and operationprediction of ac use in residential buildings, Energy Build. 188 (APR.) (2019) 137–148.
[8] G. Mark, Towards a Residential Air-Conditioner Usage Model for Australia, Energies 10 (9) (2017) 1256.
[9] J. Wang, J. Zhu, Z. Ding, et al., Typical energy-related behaviors and gender difference for cooling energy consumption, J. Cleaner Prod. 238 (2019) 117846.
[10] J. Tanimoto, A. Hagishima, State transition stochastic model for predicting off to on cooling schedule in dwellings as implemented using a multilayered artificial neural network, J. Build. Perform. Simul. 5 (1) (2012) 45–53.
[11] M. Schweiker, M. Shukuya, Comparison of theoretical and statistical models of air-conditioning-unit usage behaviour in a residential setting under Japanese climatic conditions, Build. Environ. 44 (10) (2009) 2137–2149.
[12] J. Ryu, J. Kim, W. Hong, et al., Quantifying householder tolerance of thermal discomfort before turning on air-conditioner, Energy Build. 211 (2020) 109797.
[13] J. Kim, R. De Dear, T. Parkinson, et al., Understanding patterns of adaptive comfort behaviour in the Sydney mixed-mode residential context, Energy Build. 141 (2017) 274–283.
[14] T. Hong, H. Sun, Y. Chen, et al., An Occupant Behavior Modeling Tool for CoSimulation, Energy Build. 117 (2015) 272–281.
[15] X. Zhou, D. Yan, X. Feng, et al., Influence of household air-conditioning use modes on the energy performance of residential district cooling systems, Build. Simul. 9 (4) (2016) 429–441.
[16] Y. Peng, A. Rysanek, Z. Nagy, A. Schlüter, Using machine learning techniques for occupancy-prediction-based cooling control in office buildings, Appl. Energy 211 (2018) 1343–1358.
Cite This Article
  • APA Style

    Jiawen Ren, Xin Zhou, Xing Shi, Xing Jin. (2021). Analysis of Occupants’ Air-Conditioning Opening Behaviour Based on the Survival Model. Urban and Regional Planning, 6(4), 147-154. https://doi.org/10.11648/j.urp.20210604.16

    Copy | Download

    ACS Style

    Jiawen Ren; Xin Zhou; Xing Shi; Xing Jin. Analysis of Occupants’ Air-Conditioning Opening Behaviour Based on the Survival Model. Urban Reg. Plan. 2021, 6(4), 147-154. doi: 10.11648/j.urp.20210604.16

    Copy | Download

    AMA Style

    Jiawen Ren, Xin Zhou, Xing Shi, Xing Jin. Analysis of Occupants’ Air-Conditioning Opening Behaviour Based on the Survival Model. Urban Reg Plan. 2021;6(4):147-154. doi: 10.11648/j.urp.20210604.16

    Copy | Download

  • @article{10.11648/j.urp.20210604.16,
      author = {Jiawen Ren and Xin Zhou and Xing Shi and Xing Jin},
      title = {Analysis of Occupants’ Air-Conditioning Opening Behaviour Based on the Survival Model},
      journal = {Urban and Regional Planning},
      volume = {6},
      number = {4},
      pages = {147-154},
      doi = {10.11648/j.urp.20210604.16},
      url = {https://doi.org/10.11648/j.urp.20210604.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.urp.20210604.16},
      abstract = {It is essential to predict building energy consumption through more accurate simulation of building energy consumption, and then put forward suggestions for building energy conservation. Therefore, it is a very important issue to study the variable, random and complex air conditioning usage mode. In the previous studies on air conditioning, it could be found that whether the air conditioning is on or off, it is only a mathematical function about environmental parameters. However, when we arrive at the office and feel uncomfortable, we don't open the window immediately. Instead, we put up with it for a while. In view of the above shortcomings, this study proposed a survival model based on Weibull function to predict the air-conditioning on behavior. Through the verification of the model, we found that the accuracy of the air-conditioning regulation model based on the survival model is more than 74%. We compared and verified the common three-parameter Weibull model with the survival model-based Weibull model, and found that the accuracy of the common three-parameter Weibull model was slightly higher than that of the survival model. At the same time, we analyzed the death event (tolerance temperature) of the survival model, and further improving the tolerance temperature is of great help to the accuracy of the model.},
     year = {2021}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Analysis of Occupants’ Air-Conditioning Opening Behaviour Based on the Survival Model
    AU  - Jiawen Ren
    AU  - Xin Zhou
    AU  - Xing Shi
    AU  - Xing Jin
    Y1  - 2021/11/24
    PY  - 2021
    N1  - https://doi.org/10.11648/j.urp.20210604.16
    DO  - 10.11648/j.urp.20210604.16
    T2  - Urban and Regional Planning
    JF  - Urban and Regional Planning
    JO  - Urban and Regional Planning
    SP  - 147
    EP  - 154
    PB  - Science Publishing Group
    SN  - 2575-1697
    UR  - https://doi.org/10.11648/j.urp.20210604.16
    AB  - It is essential to predict building energy consumption through more accurate simulation of building energy consumption, and then put forward suggestions for building energy conservation. Therefore, it is a very important issue to study the variable, random and complex air conditioning usage mode. In the previous studies on air conditioning, it could be found that whether the air conditioning is on or off, it is only a mathematical function about environmental parameters. However, when we arrive at the office and feel uncomfortable, we don't open the window immediately. Instead, we put up with it for a while. In view of the above shortcomings, this study proposed a survival model based on Weibull function to predict the air-conditioning on behavior. Through the verification of the model, we found that the accuracy of the air-conditioning regulation model based on the survival model is more than 74%. We compared and verified the common three-parameter Weibull model with the survival model-based Weibull model, and found that the accuracy of the common three-parameter Weibull model was slightly higher than that of the survival model. At the same time, we analyzed the death event (tolerance temperature) of the survival model, and further improving the tolerance temperature is of great help to the accuracy of the model.
    VL  - 6
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • School of Architecture, Southeast University, Nanjing, China

  • School of Architecture, Southeast University, Nanjing, China

  • Key Laboratory of Ecology and Energy-saving Study of Dense Habitat, Ministry of Education, College of Architecture and Urban Planning, Tongji University, Shanghai, China

  • School of Architecture, Southeast University, Nanjing, China

  • Sections