Skip to main content
SpringerLink
Log in

Deep Learning for Building Occupancy Estimation Using Environmental Sensors

  • Chapter
  • First Online:
Deep Learning: Algorithms and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 865))

Abstract

Building Energy efficiency has gained more and more attention in last few years. Occupancy level is a key factor for achieving building energy efficiency, which directly affects energy-related control systems in buildings. Among varieties of sensors for occupancy estimation, environmental sensors have unique properties of non-intrusion and low-cost. In general, occupancy estimation using environmental sensors contains feature engineering and learning. The traditional feature extraction requires to manually extract significant features without any guidelines. This handcrafted feature extraction process requires strong domain knowledge and will inevitably miss useful and implicit features. To solve these problems, this chapter presents a Convolutional Deep Bi-directional Long Short-Term Memory (CDBLSTM)  method that consists of a convolutional neural network with stacked architecture to automatically learn local sequential features from raw environmental sensor data from scratch. Then, the LSTM network is used to encode temporal dependencies of these local features, and the Bi-directional structure is employed to consider the past and future contexts simultaneously during feature learning. We conduct real experiments to compare the CDBLSTM and some state-of-the-art approaches for building occupancy estimation. The results indicate that the CDBLSTM approach outperforms all the state-of-the-arts.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Balaji, B., Xu, J., Nwokafor, A., Gupta, R., Agarwal, Y.: Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, p. 17. ACM (2013)

    Google Scholar 

  2. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)

    Article  Google Scholar 

  3. Candanedo, L.M., Feldheim, V.: Accurate occupancy detection of an office room from light, temperature, humidity and CO\(_2\) measurements using statistical learning models. Energy Build. 112, 28–39 (2016)

    Google Scholar 

  4. Chen, Z., Jiang, C.: Building occupancy modeling using generative adversarial network. Energy Build. 174, 372–379 (2018)

    Article  Google Scholar 

  5. Chen, Z., Jiang, C., Xie, L.: Building occupancy estimation and detection: a review. Energy Build. (2018)

    Google Scholar 

  6. Chen, Z., Masood, M.K., Soh, Y.C.: A fusion framework for occupancy estimation in office buildings based on environmental sensor data. Energy Build. 133, 790–798 (2016)

    Article  Google Scholar 

  7. Chen, Z., Soh, Y.C.: Modeling building occupancy using a novel inhomogeneous Markov chain approach. In: 2014 IEEE International Conference on Automation Science and Engineering (CASE), pp. 1079–1084. IEEE (2014)

    Google Scholar 

  8. Chen, Z., Xu, J., Soh, Y.C.: Modeling regular occupancy in commercial buildings using stochastic models. Energy Build. 103, 216–223 (2015)

    Article  Google Scholar 

  9. Chen, Z., Zhang, L., Cao, Z., Guo, J.: Distilling the knowledge from handcrafted features for human activity recognition. IEEE Trans. Ind. Inform. (2018)

    Google Scholar 

  10. Chen, Z., Zhang, L., Jiang, C., Cao, Z., Cui, W.: WiFi CSI based passive human activity recognition using attention based BLSTM. IEEE Trans. Mob. Comput. (2018)

    Google Scholar 

  11. Chen, Z., Zhao, R., Zhu, Q., Masood, M.K., Soh, Y.C., Mao, K.: Building occupancy estimation with environmental sensors via CDBLSTM. IEEE Trans. Ind. Electron. 64(12), 9549–9559 (2017)

    Article  Google Scholar 

  12. Chen, Z., Zhu, Q., Masood, M.K., Soh, Y.C.: Environmental sensors-based occupancy estimation in buildings via IHMM-MLR. IEEE Trans. Ind. Electron. 13(5), 2184–2193 (2017)

    Google Scholar 

  13. Dong, B., Andrews, B., Lam, K.P., Höynck, M., Zhang, R., Chiou, Y.S., Benitez, D.: An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network. Energy Build. 42(7), 1038–1046 (2010)

    Article  Google Scholar 

  14. Frodl, R., Tille, T.: A high-precision NDIR gas sensor for automotive applications. IEEE Sens. J. 6(6), 1697–1705 (2006)

    Article  Google Scholar 

  15. Gong, Y., Wang, L., Guo, R., Lazebnik, S.: Multi-scale orderless pooling of deep convolutional activation features. In: European Conference on Computer Vision, pp. 392–407. Springer (2014)

    Google Scholar 

  16. Hinton, G.E.: Learning multiple layers of representation. Trends Cogn. Sci. 11(10), 428–434 (2007)

    Article  Google Scholar 

  17. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  18. Iglesias, F., Palensky, P.: Profile-based control for central domestic hot water distribution. IEEE Trans. Ind. Inform. 10(1), 697–705 (2014)

    Article  Google Scholar 

  19. Jiang, C., Chen, Z., Png, L.C., Bekiroglu, K., Srinivasan, S., Su, R.: Building occupancy detection from carbon-dioxide and motion sensors. In: International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 931–936 (2018)

    Google Scholar 

  20. Jiang, C., Chen, Z., Su, R., Soh, Y.C.: Group greedy method for sensor placement. IEEE Trans. Signal Process. 67(9), 2249–2262 (2019)

    Article  MathSciNet  Google Scholar 

  21. Jiang, C., Masood, M.K., Soh, Y.C., Li, H.: Indoor occupancy estimation from carbon dioxide concentration. Energy Build. 131, 132–141 (2016)

    Article  Google Scholar 

  22. Jin, M., Jia, R., Spanos, C.J.: Virtual occupancy sensing: using smart meters to indicate your presence. IEEE Trans. Mob. Comput. 16(11), 3264–3277 (2017)

    Article  Google Scholar 

  23. Labeodan, T., Zeiler, W., Boxem, G., Zhao, Y.: Occupancy measurement in commercial office buildings for demand-driven control applications a survey and detection system evaluation. Energy Build. 93, 303–314 (2015)

    Article  Google Scholar 

  24. Leephakpreeda, T.: Adaptive occupancy-based lighting control via grey prediction. Build. Environ. 40(7), 881–886 (2005)

    Article  Google Scholar 

  25. Li, N., Calis, G., Becerik-Gerber, B.: Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations. Autom. Constr. 24, 89–99 (2012)

    Article  Google Scholar 

  26. Liu, H., Motoda, H.: Feature Extraction, Construction and Selection: A Data Mining Perspective. Springer Science & Business Media (1998)

    Google Scholar 

  27. Liu, P., Nguang, S.K., Partridge, A.: Occupancy inference using pyroelectric infrared sensors through hidden Markov models. IEEE Sens. J. 16(4), 1062–1068 (2016)

    Article  Google Scholar 

  28. Mantovani, G., Ferrarini, L.: Temperature control of a commercial building with model predictive control techniques. IEEE Trans. Ind. Electron. 62(4), 2651–2660 (2015)

    Article  Google Scholar 

  29. Masood, M.K., Jiang, C., Soh, Y.C.: A novel feature selection framework with hybrid feature-scaled extreme learning machine (HFS-ELM) for indoor occupancy estimation. Energy Build. 158, 1139–1151 (2018)

    Article  Google Scholar 

  30. Masood, M.K., Soh, Y.C., Chang, V.W.C.: Real-time occupancy estimation using environmental parameters. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2015)

    Google Scholar 

  31. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)

    Google Scholar 

  32. Parise, G., Martirano, L., Cecchini, G.: Design and energetic analysis of an advanced control upgrading existing lighting systems. IEEE Trans. Ind. Appl. 50(2), 1338–1347 (2014)

    Article  Google Scholar 

  33. Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  34. Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)

    Google Scholar 

  35. Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA Neural Netw. Mach. Learn. 4(2) (2012)

    Google Scholar 

  36. Tran, D., Tan, Y.K.: Sensorless illumination control of a networked led-lighting system using feedforward neural network. IEEE Trans. Ind. Electron. 61(4), 2113–2121 (2014)

    Article  Google Scholar 

  37. Williams, D., Hinton, G.: Learning representations by back-propagating errors. Nature 323(6088), 533–538 (1986)

    Article  Google Scholar 

  38. Yang, Z., Li, N., Becerik-Gerber, B., Orosz, M.: A systematic approach to occupancy modeling in ambient sensor-rich buildings. Simulation 90(8), 960–977 (2014)

    Article  Google Scholar 

  39. Yu, Z., Jia, L., Murphy-Hoye, M.C., Pratt, A., Tong, L.: Modeling and stochastic control for home energy management. IEEE Trans. Smart Grid 4(4), 2244–2255 (2013)

    Article  Google Scholar 

  40. Zhu, Q., Chen, Z., Masood, M.K., Soh, Y.C.: Occupancy estimation with environmental sensing via non-iterative LRF feature learning in time and frequency domains. Energy Build. 141, 125–133 (2017)

    Article  Google Scholar 

  41. Zimmermann, L., Weigel, R., Fischer, G.: Fusion of nonintrusive environmental sensors for occupancy detection in smart homes. IEEE Internet Things J. 5(4), 2343–2352 (2018)

    Article  Google Scholar 

  42. Zou, J., Zhao, Q., Yang, W., Wang, F.: Occupancy detection in the office by analyzing surveillance videos and its application to building energy conservation. Energy Build. 152, 385–398 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Infocomm Research (I2R), A*STAR, Singapore, Singapore

    Zhenghua Chen, Min Wu & Xiaoli Li

  2. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China

    Chaoyang Jiang

  3. Department of Civil Engineering, Aalto University, Espoo, Finland

    Mustafa K. Masood

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Yeng Chai Soh

Authors
  1. Zhenghua Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaoyang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mustafa K. Masood
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yeng Chai Soh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Min Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaoli Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenghua Chen .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chen, Z., Jiang, C., Masood, M.K., Soh, Y.C., Wu, M., Li, X. (2020). Deep Learning for Building Occupancy Estimation Using Environmental Sensors. In: Pedrycz, W., Chen, SM. (eds) Deep Learning: Algorithms and Applications. Studies in Computational Intelligence, vol 865. Springer, Cham. https://doi.org/10.1007/978-3-030-31760-7_11

Download citation

Publish with us

Policies and ethics

Search

Navigation