Skip to main content
Log in

A Novel Approach to Save Energy by Detecting Faulty HVACs

  • Original Contribution
  • Published:
Journal of The Institution of Engineers (India): Series B Aims and scope Submit manuscript

Abstract

Studies have shown that heating ventilation and air-conditioning (HVAC) systems are the major contributors to the high energy consumption of buildings. This has some serious deleterious effects on the environment. Thus, to reduce and optimize the energy consumption, it is important for the HVACs to work efficiently at all times. Existing techniques have focused on the optimal usage of HVACs for minimizing the energy consumption of buildings. This paper offers a pragmatic and holistic solution for saving energy in smart buildings by detecting faulty HVACs using power consumption and temperature data only. The developed approach is applied to a building consisting of forty-eight HVACs and is situated in Mumbai. A total of thirty-one HVACs were detected to be faulty. The results indicate that about 46% of energy can be saved if all the HVACs operating in the building are free of faults. This is an impressive saving in energy consumption, which demonstrates the effectiveness of this novel approach.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Demand Analysis for Cooling by Sector in India in 2027. https://aeee.in/demand-analysis-of-cooling-by-sector-in-india-in-2017-and-2027-2/

  2. International Energy Agency. India 2020 Energy Policy Review. https://webstore.iea.org/download/direct/2933?fileName=India_2020-Policy_Energy_Review.pdf , 2020.

  3. F. Al-Turjman, C. Altrjman, S. Din, A. Paul, Energy monitoring in IoT-based ad hoc networks: an overview. Comput. Electr. Eng. 76, 133–142 (2019)

    Article  Google Scholar 

  4. K. Mason, S. Grijalva, A review of reinforcement learning for autonomous building energy management. Comput. Electr. Eng. 78, 300–312 (2019)

    Article  Google Scholar 

  5. L. Perez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information. Energy Build. 40(3), 394–398 (2008)

    Article  Google Scholar 

  6. T. Downey, J. Proctor, What can 13,000 air conditioners tell us, in Proceedings of the 2002 ACEEE Summer Study on Energy Efficiency in Buildings, vol. 1, pp. 53–67, 2002.

  7. A. Agarwal, V. Munigala, K. Ramamritham, Observability: a principled approach to provisioning sensors in buildings, in Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys ’16), ACM, New York, USA, pp. 197–206, 2016.

  8. A. Agarwal, V. Munigala, K. Ramamritham, Observability: replacing sensors with inference engines, in Proceedings of the Seventh International Conference on Future Energy Systems Poster Sessions (e-Energy ’16), ACM, New York, USA, pp. 1–2, 2016

  9. A. Beghi, R. Brignoli, L. Cecchinato, G. Menegazzo, M. Rampazzo, F. Simmini, Data-driven fault detection and diagnosis for HVAC water chillers. Control. Eng. Pract. 53, 79–91 (2016)

    Article  Google Scholar 

  10. D. Gao, S. Wang, K. Shan, C. Yan, A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems. Appl. Energy 164, 1028–1038 (2016)

    Article  Google Scholar 

  11. S. Katipamula, M.R. Brambley, Review article: methods for fault detection, diagnostics, and prognostics for building systems a review, part I. HVAC&R Research 11(1), 3–25 (2005)

    Article  Google Scholar 

  12. P.M. Frank, Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: a survey and some new results. Automatica 26(3), 459–474 (1990)

    Article  Google Scholar 

  13. R. Isermann, Process fault detection based on modeling and estimation methods A survey. Automatica 20(4), 387–404 (1984)

    Article  Google Scholar 

  14. S. Li, J. Wen, A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. Energy Build. 68, 63–71 (2014)

    Article  Google Scholar 

  15. G. Karmakar, U. Arote, A. Agarwal, K. Ramamritham, Adaptive hybrid approaches to thermal modeling of building, in Proceedings of the Ninth International Conference on Future Energy Systems (e-Energy ’18), ACM, New York, USA, pp. 477–479, 2018

  16. M. Bonvini, M.D. Sohn, J. Granderson, M. Wetter, M. Ann Piette, Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques. Appl. Energy 124, 156–166 (2014)

    Article  Google Scholar 

  17. D. Dey, B. Dong, A probabilistic approach to diagnose faults of air handling units in buildings. Energy Build. 130, 177–187 (2016)

    Article  Google Scholar 

  18. M. Najafi, D.M. Auslander, P.L. Bartlett, P. Haves, M.D. Sohn, Application of machine learning in the fault diagnostics of air handling units. Appl. Energy 96, 347–358 (2012)

    Article  Google Scholar 

  19. S.M. Namburu, M.S. Azam, J. Luo, K. Choi, K.R. Pattipati, Data-driven modeling, fault diagnosis and optimal sensor selection for HVAC chillers. IEEE Trans. Autom. Sci. Eng. 4(3), 469–473 (2007)

    Article  Google Scholar 

  20. H. Shahnazari, P. Mhaskar, J.M. House, T.I. Salsbury, Modeling and fault diagnosis design for HVAC systems using recurrent neural networks. Comput. Chem. Eng. 126, 189–203 (2019)

    Article  Google Scholar 

  21. H. Teimourzadeh, F. Jabari, B. Mohammadi-Ivatloo, An augmented group search optimization algorithm for optimal cooling-load dispatch in multi-chiller plants. Comput. Electr. Eng. 85, 106 (2020)

    Article  Google Scholar 

  22. W.J.N. Turner, A. Staino, B. Basu, Residential HVAC fault detection using a system identification approach. Energy Build. 151, 1–17 (2017)

    Article  Google Scholar 

  23. Y. Yu, D. Woradechjumroen, D. Yu, A review of fault detection and diagnosis methodologies on air-handling units. Energy and Build. 82, 550–562 (2014)

    Article  Google Scholar 

  24. Y. Zhao, S. Wang, F. Xiao, Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD). Appl. Energy 112, 1041–1048 (2013)

    Article  Google Scholar 

  25. V. Martinez-Viol, E. M. Urbano, K. Kampouropoulos, M. Delgado-Prieto, L. Romeral, Support vector machine based novelty detection and FDD framework applied to building AHU systems, in Proceedings of the 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria, 2020, pp. 1749–1754, 2020

  26. S. Gharsellaoui, M. Mansouri, M. Trabelsi, M.F. Harkat, S.S. Refaat, H. Messaoud, Interval-valued features based machine learning technique for fault detection and diagnosis of uncertain HVAC systems. IEEE Access 8, 171892–171902 (2020)

    Article  Google Scholar 

  27. C.P. Dowling, B. Zhang, Transfer learning for HVAC system fault detection, in Proceedings of the American Control Conference (ACC), Denver, CO, USA, pp. 3879–3885, 2020

  28. Schneider Electric Smartmeter EM6400. https://www.schneider-electric.co.in/library/SCHNEIDER_ELECTRIC/SE_LOCAL/APS/203086_FC3E/EM_6400_Manual Firmware_03.02.xx_.pdf

  29. DS18B20 Digital Thermometer. https://datasheets.maximintegrated.com/en/ds/DS18B20.pdf

Download references

Funding

Not Applicable.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anshul Agarwal.

Ethics declarations

Conflict of interest

The author declares that there is no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agarwal, A. A Novel Approach to Save Energy by Detecting Faulty HVACs. J. Inst. Eng. India Ser. B 103, 305–311 (2022). https://doi.org/10.1007/s40031-021-00666-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40031-021-00666-7

Keywords

Navigation