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The Monitoring and Management of an Operating Environment to Enhance the Safety of a Container-Type Energy Storage System.

Sensors (Basel, Switzerland)ยท2023
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Updated: Aug 8, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Occupancy-Based Energy Consumption Estimation Improvement through Deep Learning.

Mi-Lim Kim1, Keon-Jun Park2, Sung-Yong Son1

  • 1Department of Next Generation Smart Energy System Convergence, Gachon University, Seongnam-si 13120, Republic of Korea.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary

Accurate building occupancy estimation is crucial for energy analysis. Deep learning models significantly improved occupancy count accuracy and energy consumption predictions, reducing errors by over 70% and 5% respectively.

Keywords:
building energydeep learningenergy consumptionestimation improvementoccupancy

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Area of Science:

  • Building energy analysis
  • Occupancy sensing technology
  • Artificial intelligence in energy management

Background:

  • Building energy consumption is substantial, often exceeding 50% of total electricity usage.
  • Occupancy levels are a key driver of building energy demand.
  • Inaccurate occupancy data, due to sensor limitations and communication issues, hinders precise energy analysis.

Purpose of the Study:

  • To analyze measurement errors in building occupancy counts.
  • To develop a deep learning model for accurate occupancy estimation.
  • To predict building energy consumption using both measured and estimated occupancy data.

Main Methods:

  • Occupancy was initially measured using object recognition cameras.
  • Manual aggregation supplemented camera data.
  • A deep learning model was employed to estimate true occupancy counts by analyzing measurement errors.
  • Deep learning was also used to predict energy consumption based on occupancy data.

Main Results:

  • The deep learning model achieved a Root Mean Square Error (RMSE) of 1.9 for occupancy estimation, a 71.1% improvement over original sensing methods.
  • Energy consumption prediction using estimated occupancy yielded an RMSE of 56.0, a 5.2% improvement over original estimations.

Conclusions:

  • Deep learning models can significantly enhance the accuracy of building occupancy estimation.
  • Improved occupancy data leads to more precise building energy consumption predictions.
  • This approach offers a viable solution for optimizing energy management in buildings.