A machine learning-guided semi-empirical model for predicting single-sided natural ventilation rates

• Combines machine learning with semi-empirical models to enhance the prediction accuracy of natural ventilation rates. • Validates the model using real-time data from HouseZero® livelab, demonstrating its practical applicability. • Machine learning models, particularly the random forest regressor is suitable for predicting C p . • On top of the wind information, temperature and relative humidity can be utilized to predict C p . • The approach can be used to optimize natural ventilation in buildings, improving energy efficiency and indoor air quality. The state-of-the-art natural ventilation models were developed for either single-sided, or cross ventilation mode, or buoyancy-driven ventilation. Natural ventilation (NV) of a single zone may vary between different modes in different seasons depending on the design and the operation of other building systems. This paper tailors the machine learning embedded semi-empirical models to predict the natural ventilation rate in a single zone. The process of model development consists of two parts: 1) semi-empirical model development for single-sided ventilation with a local context 2) machine learning driven component to accurately predict localized the specific lab condition with comparison to the existing methods. By taking a case study, the series of steps were taken to validate model accuracy with an estimated flowrate in given window operable areas. Firstly, the contextual inputs and localized wind speed as well as window models were investigated. Finally, we developed a machine learning model to predict the localized lab environment by using pressure sensor’s data on façade. The random forest model was trained and fine-tuned to predict localized pressure coefficients (Cp). Over 75 % of the predicted values fall within the model’s ± 1 standard deviation credible interval, demonstrating not only high predictive reliability but also suitability for integration into empirical ventilation models. These results highlight the model’s potential as a robust input generator for semi-empirical frameworks with locally collected weather data, particularly in applications involving window operation control and site-specific model calibration.