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Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction.

Haosen Qin1, Zhen Yu2, Tailu Li1

  • 1Tianjin Key Laboratory of Clean Energy and Pollutant Control, School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 400301, China.

International Journal of Environmental Research and Public Health
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic programming method for intelligent heating, ventilation, and air conditioning (HVAC) control. The proposed approach optimizes thermal comfort and reduces energy consumption by 35.1% compared to traditional expert rules.

Keywords:
HVAC systemcompetitive learningdynamic programmingmodel predictive controlnearly zero energy buildingsimulation

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Area of Science:

  • Building energy systems
  • Artificial intelligence in HVAC
  • Sustainable building technologies

Background:

  • Balancing occupant comfort, lifestyle, and HVAC costs requires intelligent control.
  • Existing HVAC control methods often lack adaptability and optimal performance.
  • Dynamic programming offers a potential solution for complex control problems.

Purpose of the Study:

  • To develop and evaluate a novel heating control method for HVAC systems using dynamic programming.
  • To optimize the balance between user comfort, lifestyle, and energy costs.
  • To address the limitations of traditional control strategies in HVAC applications.

Main Methods:

  • Building modeling using three machine learning techniques, selected via statistical performance metrics.
  • Formulating HVAC control as a constrained optimization problem solved with dynamic programming.
  • Introducing 'thermal energy storage in building' to overcome dynamic programming's no-aftereffect requirement.

Main Results:

  • The proposed dynamic programming method outperforms expert rules in maintaining thermal environments.
  • Achieved a 35.1% reduction in energy consumption and emissions compared to the 'baseline-20 °C' expert rule.
  • Demonstrated acceptable comfort violation levels while significantly improving energy efficiency.

Conclusions:

  • Dynamic programming provides an effective framework for intelligent HVAC control.
  • The proposed method offers a significant improvement in energy savings and emission reduction.
  • This approach enables a better balance between occupant comfort and operational costs.