NVAPF: An adaptive particle filter algorithm for CO2-based natural ventilation rate estimation with high temporal resolution and stability

Abstract

CO₂-based ventilation rate estimation techniques have been widely used in relevant studies in naturally ventilated educational buildings. Such techniques are non-invasive, low-cost, simple, accurate, and do not interfere with the activities of indoor occupants. However, the estimation is significantly affected by the CO₂ measurement noise, the uncertainties associated with CO₂ generation rate, and complex natural ventilation dynamics. Existing techniques were found to have limited capabilities to deal with these aspects. Therefore, this research proposed an adaptive particle filter algorithm for CO₂-based natural ventilation rate estimation and validated it through a case study in an educational building. Compared with the existing transient mass balance model and the extended Kalman filter technique, the estimation stability has been improved by nearly 6 times and 3 times, respectively. More importantly, the proposed algorithm is significantly more robust to abrupt changes in indoor CO₂ and can effectively avoid large drifts in the estimated ventilation rate due to sudden window opening and sudden changes in room occupancy, demonstrating great practical applicability for real-time estimation with a high temporal resolution of 1 minute. To help relevant users with practical applications, the study also analyzed the algorithm parameter settings and the impact of simplification strategies commonly used in relevant studies, such as the use of a fixed outdoor CO₂ concentration, an averaged CO₂ generation rate, and an assumed constant room occupancy. Finally, considering that applying the proposed algorithm requires programming skills, an open, user-friendly software has been developed for relevant users for a convenient implementation.