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Measured reductions in electricity use for cooling due to model predictive control of natural ventilation

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  • Simões, João Carlos
  • da Graça, Guilherme Carrilho

Abstract

The continuous increase in electricity use for cooling of buildings is unsustainable. The combination of natural ventilation (NV) with traditional mechanical cooling systems can be part of the solution if an effective control system is able to combine the two systems while maintaining indoor air quality and thermal comfort. Currently used rule-based heuristic controls lack flexibility to tackle this control challenge, while advanced methods like reinforcement learning require substantial computational resources and extensive training data. In response to this challenge, this paper presents a real-world operation deployment of Model Predictive Control (MPC) MPC framework for natural ventilation control. The proposed MPC approach uses a resistance-capacitance thermal zone model coupled with a previously developed artificial neural network predictor (ANN) of NV airflow.

Suggested Citation

  • Simões, João Carlos & da Graça, Guilherme Carrilho, 2026. "Measured reductions in electricity use for cooling due to model predictive control of natural ventilation," Applied Energy, Elsevier, vol. 417(C).
    • Handle: RePEc:eee:appene:v:417:y:2026:i:c:s0306261926006616
    DOI: 10.1016/j.apenergy.2026.128009
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