Skip to main content

Advertisement

SpringerLink
Log in

PECMS: modeling a personalized energy and comfort management system based on residents’ behavior anticipation in smart home

  • Original Article
  • Published:
Journal of Reliable Intelligent Environments Aims and scope Submit manuscript

Abstract

Indoor electrical systems are aimed to provide comfort to the occupants. However, their operation is contingent on the presence or needs of the residents. Hence, to optimize energy consumption and guarantee the desired comfort level of residents, any indoor energy control system should consider the occupancy dynamism within houses and the occupants' behavior patterns. Moreover, there is a growing demand for localized and personalized comfort controls in residential buildings to improve the occupants’ satisfaction. This paper presents a Personalized Energy and Comfort Management System (PECMS) that optimizes building energy consumption and meanwhile maintains residents’ intended comfort levels by predicting their trajectories. Considering home thermal characterization, PECMS coordinates the building system devices and residents by anticipating residents’ behavior using an activity mining and tracking method. With this capability, efficient scheduling of the electrical devices would be achieved. PECMS is simulated and tested on a dataset from a real-world smart home project, including the home’s actual thermal zones, temperatures, and residents’ preferences. Comparative analysis is conducted to evaluate PECMS against existing methods and systems, showcasing its effectiveness in achieving the desired trade-off between energy consumption and occupant comfort levels.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Availability of data and material

Available upon request.

References

  1. Revel GM, Arnesano M, Pietroni F, Frick J, Reicher M, Schmitt K, Huber J, Ebermann M, Battista U, Alessi F (2015) Cost-effective technologies to control indoor air quality and comfort in energy efficient building retrofitting. Environ Eng Manag J 14(7):1487–1494. https://doi.org/10.30638/eemj.2015.160

    Article  Google Scholar 

  2. Peng Y, Rysanek A, Nagy Z, Schlüter A (2018) Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Appl Energy 211(December 2017):1343–1358. https://doi.org/10.1016/j.apenergy.2017.12.002

    Article  Google Scholar 

  3. Klein L, Kwak J-Y, Kavulya G, Jazizadeh F, Becerik-gerber B, Varakantham P, Tambe M (2012) Coordinating occupant behavior for building energy and comfort management using multi-agent systems. Autom Constr 22:525–536. https://doi.org/10.1016/j.autcon.2011.11.012

    Article  Google Scholar 

  4. Wang W, Chen J, Hong T (2018) Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings. Autom Constr 94(June):233–243. https://doi.org/10.1016/j.autcon.2018.07.007

    Article  Google Scholar 

  5. Chang W-K, Hong T (2013) Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data. Build Simul. https://doi.org/10.1007/s12273-013-0106-y

    Article  Google Scholar 

  6. Raeiszadeh M, Tahayori H, Visconti A (2019) Discovering varying patterns of Normal and interleaved ADLs in smart homes. Appl Intell 49(12):4175–4188. https://doi.org/10.1007/s10489-019-01493-6

    Article  Google Scholar 

  7. Elie Azar SP (2017) Human behavior and energy consumption in buildings: an integrated agent-based modeling and building performance simulation framework. Department of Engineering Systems and Management Masdar Institute of Science and Technology. Building simulation, pp 482–487

  8. Webber CA, Roberson JA, McWhinney MC, Brown RE, Pinckard MJ, Busch JF (2006) After-hours power status of office equipment in the USA. Energy 31(14):2823–2838

    Article  Google Scholar 

  9. Sanchez M, Webber C, Brown R, Busch J, Pinckard M, Roberson J (2007) Space heaters, computers, cell phone chargers: how plugged in are commercial buildings? Lawrence Berkeley National Lab, (LBNL-62397), Berkeley, CA (United States)

  10. Ioannou A, Itard LC (2015) Energy performance and comfort in residential buildings: sensitivity for building parameters and occupancy. Energy Build 92:216–233. https://doi.org/10.1016/j.enbuild.2015.01.055

    Article  Google Scholar 

  11. Yousefi F, Gholipour Y, Yan W (2017) A study of the impact of occupant behaviors on energy performance of building envelopes using occupants’ data. Energy Build 148:182–198. https://doi.org/10.1016/j.enbuild.2017.04.085

    Article  Google Scholar 

  12. Sun K, Hong T (2017) A simulation approach to estimate energy savings potential of occupant behavior measures. Energy Build 136:43–62. https://doi.org/10.1016/j.enbuild.2016.12.010

    Article  Google Scholar 

  13. Ahmadi-Karvigh S, Ghahramani A, Becerik-Gerber B, Soibelman L (2018) Real-time activity recognition for energy efficiency in buildings. Appl Energy 211:146–160. https://doi.org/10.1016/j.apenergy.2017.11.055

    Article  Google Scholar 

  14. Anastasiadi C, Dounis AI (2018) Co-simulation of fuzzy control in buildings and the HVAC system using BCVTB. Adv Build Energy Res 12(2):195–216. https://doi.org/10.1080/17512549.2017.1279077

    Article  Google Scholar 

  15. Deng Z, Chen Q (2019) Simulating the impact of occupant behavior on energy use of HVAC systems by implementing a behavioral artificial neural network model. Energy Build 198:216–227. https://doi.org/10.1016/j.enbuild.2019.06.015

    Article  Google Scholar 

  16. Jia M, Srinivasan RS, Ries R, Weyer N, Bharathy G (2019) A systematic development and validation approach to a novel agent-based modeling of occupant behaviors in commercial buildings. Energy Build 199:352–367. https://doi.org/10.1016/j.enbuild.2019.07.009

    Article  Google Scholar 

  17. Wei Y, Xia L, Pan S, Wu J, Zhang X, Han M, Zhang W, Xie J, Li Q (2019) Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks. Appl Energy 240:276–294. https://doi.org/10.1016/j.apenergy.2019.02.056

    Article  Google Scholar 

  18. Jia M, Srinivasan R (2020) Building performance evaluation using coupled simulation of energyplus and an occupant behavior model. Sustainability (Switzerland). https://doi.org/10.3390/su12104086

    Article  Google Scholar 

  19. Dziedzic JW, Yan D, Sun H, Novakovic V (2020) Building occupant transient agent-based model—movement module. Appl Energy 261(7491):114417. https://doi.org/10.1016/j.apenergy.2019.114417

    Article  Google Scholar 

  20. Salimi S, Hammad A (2020) Optimizing energy consumption and occupants comfort in open-plan offices using local control based on occupancy dynamic data. Build Environ 176:106818. https://doi.org/10.1016/j.buildenv.2020.106818

    Article  Google Scholar 

  21. Underhill LJ, Dols WS, Lee SK, Fabian MP, Levy JI (2020) Quantifying the impact of housing interventions on indoor air quality and energy consumption using coupled simulation models. J Eposure Sci Environ Epidemiol 30(3):436–447. https://doi.org/10.1038/s41370-019-0197-3

    Article  Google Scholar 

  22. Park H (2020) Human comfort-based-home energy management for demand response participation. Energies (Basel). https://doi.org/10.3390/en13102463

    Article  Google Scholar 

  23. Kwon K, Lee S, Kim S (2022) AI-based home energy management system considering energy efficiency and resident satisfaction. IEEE Internet Things J 9(2):1608–1621. https://doi.org/10.1109/JIOT.2021.3104830

    Article  Google Scholar 

  24. Jin Y, Yan D, Kang X, Chong A, Sun H, Zhan S (2021) Forecasting building occupancy: a temporal-sequential analysis and machine learning integrated approach. Energy Build. https://doi.org/10.1016/j.enbuild.2021.111362

    Article  Google Scholar 

  25. Le Cam A, Southernwood J, Ring D, Clarke D, Creedon R (2021) Impact of demand response on occupants’ thermal comfort in a leisure center. Energy Eff 14:91. https://doi.org/10.1007/s12053-021-09965-w

    Article  Google Scholar 

  26. Fanger PO (1970) Thermal comfort: analysis and applications in environmental engineering. Danish Technical Press, Copenhagen

    Google Scholar 

  27. U.S. Department of Energy (2020) EnergyPlus v9.1.0—input output reference. p. 484–485

  28. Zupan D (2014) Smart-home energy management in the context of occupants’ activity. Informatica 38:171–180

    Google Scholar 

  29. Wilcox S, Marion W (2008) User manual for TMY3 data sets. National Renewable Energy Laboratory, Golden

    Book  Google Scholar 

  30. Autodesk (2010). Sustainable design analysis and building information modeling. Autodesk-EcotectTM. pp. 1–10

  31. Tapia EM, Intille SS, Larson K (2004) Activity recognisation in Home Using Simple state changing sensors. Pervasive Comput 3001:158–175. https://doi.org/10.1007/978-3-540-24646-6_10

    Article  Google Scholar 

  32. Skalko S, Cabot PW (2003) Design of low-rise approved by the ASHRAE Standards Committee on June. 8400. pp 1–6

Download references

Funding

Authors have not received any funding for this research.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering and IT, Shiraz University, Shiraz, Iran

    Mahsa Raeiszadeh & Hooman Tahayori

  2. School of Computing and Engineering, University of West London, London, UK

    Ali Bahadori-jahromi

Authors
  1. Mahsa Raeiszadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hooman Tahayori
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Bahadori-jahromi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study concept and design: MR and HT; analysis and interpretation of data: MR; drafting of the manuscript: MR and HT; critical revision of the manuscript for important intellectual content and verification of the analytical methods: AB-J and HT.

Corresponding author

Correspondence to Hooman Tahayori.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raeiszadeh, M., Tahayori, H. & Bahadori-jahromi, A. PECMS: modeling a personalized energy and comfort management system based on residents’ behavior anticipation in smart home. J Reliable Intell Environ 10, 123–136 (2024). https://doi.org/10.1007/s40860-023-00206-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40860-023-00206-8

Keywords

Search

Navigation