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Occupancy detection via thermal sensors for energy consumption reduction

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Abstract

With the emergence of Internet of Things (IoT), the usage of sensors for controlling and monitoring remote devices to achieve sustainability has gained researchers’ interest. It has been observed that buildings are one of the largest consumers of energy hence, effective measures are required to achieve sustainability. In this sector, substantial amount of energy is used by HVAC (Heating, Ventilation and Air Conditioning) systems to offer ease for occupants. In most cases, HVAC systems of these buildings run on fixed schedules and do not provide any satisfactory control, based on detailed occupancy information. In this paper, a new solution is presented for estimating occupancy using network of thermal sensor arrays. The system provides near real time actionable information for controlling HVAC system and conditioning the rooms based on usage. The proposed system is a network of wired sensors, wireless sensors and gateway nodes, working together. Energy readings estimate the battery life of over two years, while working accurately. The system shows potential energy savings of 10% to 15%. Recurrent neural network are also used to train the model and compared with the proposed method. We conclude this new approach remarkably improves the results of occupancy detection using network of thermal sensor arrays.

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References

  1. Arvidsson S, Gullstrand M, Sirmacek B, Riveiro M (2021) Sensor fusion and convolutional neural networks for indoor occupancy prediction using multiple low-cost low-resolution heat sensor data. Sensors 21(4):1036

    Article  Google Scholar 

  2. Baroffio L, Bondi L, Cesana M, Redondi AE, Tagliasacchi M (2015) A visual sensor network for parking lot occupancy detection in smart cities. In: 2015 IEEE 2nd world forum on internet of things (WF-iot), pp 745–750. IEEE

  3. Caicedo D, Pandharipande A (2012) Ultrasonic array sensor for indoor presence detection. In: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp 175–179. IEEE

  4. Clifford C (1997) Federspiel. Estimating the inputs of gas transport processes in buildings. IEEE Trans Control Syst Technol 5(5):480–489

    Article  Google Scholar 

  5. D’Oca S, Hong T (2015) Occupancy schedules learning process through a data mining framework. Energy Build 88:395–408

    Article  Google Scholar 

  6. Dhuri V, Khan A, Kamtekar Y, Patel D, Jaiswal I (2021) Real-time parking lot occupancy detection system with vgg16 deep neural network using decentralized processing for public, private parking facilities. In: 2021 International conference on advances in electrical, computing, communication and sustainable technologies (ICAECT), pp 1–8. IEEE

  7. Diraco G, Leone A, Siciliano P (2015) People occupancy detection and profiling with 3d depth sensors for building energy management. Energy Build 92:246–266

    Article  Google Scholar 

  8. Dodier RH, Henze GP, Tiller DK, Guo X (2006) Building occupancy detection through sensor belief networks. Energy Build 38(9):1033–1043

    Article  Google Scholar 

  9. Domdouzis K, Kumar B, Anumba C (2007) Radio-frequency identification (rfid) applications: a brief introduction. Adv Eng Inform 21(4):350–355

    Article  Google Scholar 

  10. Dong B, Lam KP (2011) Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network. J Build Perform Simul 4(4):359–369

    Article  Google Scholar 

  11. Fatema N, Malik Ht (2021) Data-driven occupancy detection hybrid model using particle swarm optimization based artificial neural network. In: metaheuristic and evolutionary computation: algorithms and applications, pp 283–297. Springer

  12. Feng C, Mehmani A, Zhang J (2020) Deep learning-based real-time building occupancy detection using ami data. IEEE Trans Smart Grid 11 (5):4490–4501

    Article  Google Scholar 

  13. Gu Y, Lo A, Niemegeers I (2009) A survey of indoor positioning systems for wireless personal networks. IEEE Commun Surv Tutor 11(1):13–32

    Article  Google Scholar 

  14. Gul MS, Patidar S (2015) Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build 87:155–165

    Article  Google Scholar 

  15. Hailemariam E, Goldstein R, Attar R, Khan A (2011) Real-time occupancy detection using decision trees with multiple sensor types. In: Proceedings of the 2011 Symposium on simulation for architecture and urban design, pp 141–148

  16. Hallberg J, Nilsson M, Synnes K (2003) Positioning with bluetooth. In: 10th international conference on telecommunications, 2003. ICT 2003., vol 2, pp 954–958. IEEE

  17. Hallberg J, Nugent C, Davies R, Donnelly M (2009) Localisation of forgotten items using rfid technology. In: 2009 9th international conference on information technology and applications in biomedicine, pp 1–4. IEEE

  18. Konis K, Blessenohl S, Kedia N, Rahane V (2020) Trojansense, a participatory sensing framework for occupant-aware management of thermal comfort in campus buildings. Build Environ 169:106588

    Article  Google Scholar 

  19. Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern Part C Appl Rev 37(6):1067–1080

    Article  Google Scholar 

  20. Liu D, Guan X, Du Y, Zhao Q (2013) Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors. Meas Sci Technol 24(7):074023

    Article  Google Scholar 

  21. Naseer A, Yasir T, Azhar A, Shakeel T, Zafar K (2021) Computer-aided brain tumor diagnosis: performance evaluation of deep learner cnn using augmented brain mri. International Journal of Biomedical Imaging, 2021

  22. Naseer Asma, Zafar Kashif (2018) Comparative analysis of raw images and meta feature based urdu ocr using cnn and lstm. Int J Adv Comput Sci Appl 9 (1):419–424

    Google Scholar 

  23. Naseer A, Zafar K (2019) Meta features-based scale invariant ocr decision making using lstm-rnn. Comput Math Org Theor 25(2):165–183

    Article  Google Scholar 

  24. Occupancy Dataset (2020) https://github.com/holoviz/panel/blob/master/examples/assets/occupancy.csv. Accessed 01 Jun 2021

  25. Payne FW, McGowan JJ (2012) Energy management and control systems handbook. Springer Science & Business Media

  26. Platner BP, Mudge PH (1997) Occupancy detector. US Patent 5:701–117

    Google Scholar 

  27. Tamoor M, Younas I (2021) Automatic segmentation of medical images using a novel harris hawk optimization method and an active contour model. Journal of X-Ray Science and Technology, (Preprint): 1–19

  28. Wahl F, Milenkovic M, Amft O (2012) A green autonomous self-sustaining sensor node for counting people in office environments. In: 2012 5th european DSP education and research conference (EDERC), pp 203–207. IEEE

  29. Wang S, Burnett J, Chong H (1999) Experimental validation of co2-based occupancy detection for demand-controlled ventilation. Indoor Built Environ 8(6):377–391

    Article  Google Scholar 

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Authors and Affiliations

  1. National University of Computer and Emerging Sciences, Lahore, Pakistan

    Asma Naseer

  2. Forman Christian College, Lahore, Pakistan

    Maria Tamoor & Ayesha Khan

  3. UMT, Lahore, Pakistan

    Dawood Akram & Zohaib Javaid

Authors
  1. Asma Naseer
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  2. Maria Tamoor
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  3. Ayesha Khan
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  4. Dawood Akram
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  5. Zohaib Javaid
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Corresponding author

Correspondence to Maria Tamoor.

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This paper will be of interest to the readership of this Journal. As the corresponding author and first author of the research work, I hereby confirm that the manuscript is entirely original, has not been copyrighted, published, submitted, or accepted for publication elsewhere.

I declare that we have no financial or other relationships that could be construed as a conflict of interest and that all sources of financial support for this study have been disclosed and are indicated in the acknowledgments.

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Naseer, A., Tamoor, M., Khan, A. et al. Occupancy detection via thermal sensors for energy consumption reduction. Multimed Tools Appl 83, 4915–4928 (2024). https://doi.org/10.1007/s11042-023-15553-0

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  • DOI: https://doi.org/10.1007/s11042-023-15553-0

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