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Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a higher temperature. When the...
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Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
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Data-Driven Living Spaces' Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification.

Roozbeh Sadeghian Broujeny1, Kurosh Madani1, Abdennasser Chebira1

  • 1Université Paris-Est, LISSI Laboratory EA 3956, Senart-FB Institute of Technology, Campus de Senart, 36-37 Rue Charpak-F-77567 Lieusaint, France.

Sensors (Basel, Switzerland)
|February 22, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid machine learning approach for modeling and controlling building heating systems, considering occupant presence. The method enhances energy efficiency in smart buildings by accurately predicting heating dynamics.

Keywords:
artificial neural networkblack box modelingenergy efficiencysmart buildingsystem identification

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Area of Science:

  • Building energy systems
  • Smart building energy management systems (SBEMS)
  • Machine learning applications in HVAC

Background:

  • Heating control in buildings is complex due to nonlinear dynamics.
  • Current smart building energy management systems (SBEMS) often use conventional control strategies due to model limitations.
  • Occupancy significantly impacts building heating needs and control strategies.

Purpose of the Study:

  • To develop and implement a data-driven, machine learning-based model for identifying the dynamic heating behavior of living spaces.
  • To incorporate the effect of space occupancy into the heating model and control strategy.
  • To improve the accuracy and adaptability of heating control in smart buildings.

Main Methods:

  • Utilized a hybrid approach combining a nonlinear autoregressive exogenous (NARX) model for time-series forecasting and a multi-layer perceptron (MLP) for learning and generalization.
  • Applied a data-driven machine learning strategy to model the dynamic heating conduct of real living spaces.
  • Integrated occupancy data into the modeling process to account for real-world usage patterns.

Main Results:

  • Successfully modeled the dynamic heating behavior of living spaces in a real five-floor building.
  • Demonstrated the accuracy and effectiveness of the hybrid machine learning approach in capturing complex heating dynamics.
  • Validated the model's performance in a practical setting, considering actual building occupancy.

Conclusions:

  • The proposed hybrid machine learning model provides a competent solution for the complex challenge of modeling and controlling building heating systems.
  • Incorporating occupancy data significantly enhances the model's ability to represent real-world heating dynamics.
  • This approach offers a promising pathway for optimizing energy efficiency in smart buildings through adaptive heating control.