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Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions.

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Summary
This summary is machine-generated.

Smart buildings can reduce energy waste by using Internet of Things (IoT) sensors to detect and predict occupant presence. This technology enables efficient energy management and improved indoor environmental monitoring.

Keywords:
artificial intelligencecognitive buildingsestimation and predictioninternet of thingsmachine learningoccupancy detectionsmart buildings

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Area of Science:

  • Building Science
  • Computer Science
  • Energy Systems

Background:

  • Significant global energy consumption occurs in buildings, with substantial waste due to inefficient appliance usage and occupancy.
  • Smart and cognitive buildings are essential for optimizing energy use through advanced sensing technologies.

Purpose of the Study:

  • To provide a comprehensive overview of recent Internet of Things (IoT) advancements for indoor environmental monitoring and occupancy detection.
  • To critically examine the strengths and limitations of various technologies in predicting occupant behavior.
  • To explore data processing methods and future prediction techniques for building occupancy.

Main Methods:

  • Review of recent Internet of Things (IoT) technologies for indoor environmental monitoring.
  • Analysis of sensory approaches for gathering occupancy data.
  • Examination of data processing and prediction methods for occupant behavior.

Main Results:

  • IoT technologies are crucial for collecting indoor environmental data and determining occupant presence and numbers.
  • Various technologies offer different strengths and limitations for predicting occupant behavior.
  • Challenges include optimizing sensor and radar placement for accurate occupancy estimation.

Conclusions:

  • Leveraging IoT for occupancy detection and prediction is key to developing smart buildings that reduce energy waste.
  • Future directions include enhancing data security and promoting energy-efficient practices through occupant localization and activity monitoring.
  • This work surveys recent sensory approaches and reviews existing methods for occupancy estimation in buildings.