An artificial neural network model using outdoor environmental parameters and residential building characteristics for predicting the nighttime natural ventilation effect

• Published in: Building and environment Vol. 159; p. 106139
• Main Authors: Dai, Xilei, Liu, Junjie, Zhang, Xin, Chen, Wenhua
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.07.2019; Elsevier BV
• Subjects: Air quality; Apartments; Artificial neural network model; Artificial neural networks; Bedrooms; Carbon dioxide; Energy consumption; Floors; Human behaviour; Indoor air pollution; Indoor air quality; Indoor carbon dioxide; Indoor environments; Machine learning; Mathematical models; Model accuracy; Neural networks; Night; Nighttime; Outdoor parameters; Parameters; Residential buildings; Temperature effects; Training; Ventilation; Ventilation effect; Wind speed; Human behaviour; Ventilation effect; Indoor carbon dioxide; Artificial neural network model; Machine learning; Outdoor parameters
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2019.05.017

The natural ventilation rate in bedrooms at night has been found to be insufficient at times. To create good indoor air quality with minimal energy consumption, we need to know when the ventilation rate will be insufficient. To achieve this goal, we monitored indoor CO2 concentrations in bedrooms in 24 apartments, together with related outdoor parameters. With this dataset, we built an artificial neural network (ANN) model to predict when the ventilation rate would be insufficient. The sample sizes for the training set and test set are 2760 sample days and 690 sample days, respectively. The overall accuracy levels of this ANN model are 80.2% and 79.3% for the training set and test set, respectively. According to the model, the indoor CO2 concentration level is significantly affected by the building floor on which the apartment is located and nighttime outdoor temperature. Apartments on an upper floor usually had lower probability of the indoor CO2 concentration reaching 1000 ppm than apartments on a lower floor. Furthermore, the probability dropped to its lowest point at 15–20∘C, and it rose significantly when the nighttime outdoor temperature increased or decreased. Increasing wind strength decreased the probability only when the outdoor temperature was greater than approximately 20∘C. In contrast, when the temperature was low, increasing wind strength may have caused the probability to increase because of the uncomfortable sensation created by increasing wind velocity. Throughout the year, the probability is estimated to be high in the middle of summer and winter. •We developed a model to predict the ventilation effect for residential buildings.•Window-opening behaviour was considered to predict the actual ventilation effect.•We identified the change pattern of the probability of insufficient ventilation.