Assessing and predicting indoor environmental quality in 13 naturally ventilated urban residential dwellings

Abstract

This research presents the assessments of IEQ in 13 naturally ventilated urban residential dwellings based on the long-term monitoring of CO2, PM2.5, formaldehyde, temperature, and relative humidity within a year. In addition to the costly monitoring techniques, we utilized time series prediction methods as an economical and efficient approach to comprehend the quality of indoor environment. Nevertheless, choosing a prediction method proves to be a challenge due to the inconclusive nature of the advantages and disadvantages associated with various methodologies. Thus, we conducted a comprehensive comparison of the LSTM, SVM, ARIMA, and BPNN algorithms for predicting IEQ. The results indicate that for short-term prediction (one day), the SVM algorithm has the shortest fitting time, the program occupies the least memory, and the overfitting problem is not readily apparent, while the prediction accuracy is acceptable. Long-term (one year) predictions can be executed with greater precision using LSTM. In regard to robustness, prediction time and accuracy of SVM in different dwellings varies greatly and the robustness is weak. When considering parameters, the discrepancy between the prediction error of PM2.5 and the sensor error is more pronounced, resulting in an even lower level of predictability. As for the input data, it was observed that the computation time of the four algorithms increased linearly with the sample size. While the accuracy of predictions is predominantly influenced by the sampling frequency of the original data. The investigation results will benefit future decision-making and control strategies regarding the enhancement of thermal comfort and indoor air quality.