Model predictive control of short-term winter natural ventilation in a smart building using machine learning algorithms

Abstract

Natural ventilation in buildings has the potential to reduce building energy consumption and the incidence of sick building syndrome. However, controlling natural ventilation is often challenging. For instance, natural ventilation through operable windows may cause cold draughts for the occupants in winter. This paper presents the key findings of a novel machine learning-based model predicted control of single-sided natural ventilation in a retrofitted four-story smart building in Cambridge, Massachusetts, in the northeast region of the United States. A data-driven methodology is used to realize this natural ventilation modeling, including data collection, system dynamics identification, and control formulation. The prediction model of indoor CO2 and room air temperature is built through a learning process. The model predictive control (MPC) formulation step combines thermal comfort and indoor air quality requirements. Finally, the natural ventilation MPC is tested in the real building, and the results showed that indoor air quality and thermal comfort are maintained successfully, without increasing significantly the heating energy consumption in winter. This scalable data-driven methodology can potentially be applied to natural ventilation in smart buildings.