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Deep Learning for Building Occupancy Estimation Using Environmental Sensors

  • Zhenghua ChenEmail author
  • Chaoyang Jiang
  • Mustafa K. Masood
  • Yeng Chai Soh
  • Min Wu
  • Xiaoli Li
Chapter
  • 455 Downloads
Part of the Studies in Computational Intelligence book series (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.

Keywords

Deep learning Building occupancy estimation Environmental sensors CDBLSTM 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhenghua Chen
    • 1
    Email author
  • Chaoyang Jiang
    • 2
  • Mustafa K. Masood
    • 3
  • Yeng Chai Soh
    • 4
  • Min Wu
    • 1
  • Xiaoli Li
    • 1
  1. 1.Institute for Infocomm Research (I2R), A*STARSingaporeSingapore
  2. 2.School of Mechanical EngineeringBeijing Institute of TechnologyBeijingChina
  3. 3.Department of Civil EngineeringAalto UniversityEspooFinland
  4. 4.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

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