# A Practical and Adaptive Approach to Predicting Indoor CO2
## 2 Related Work  
In [14], the authors evaluated the use of relative humidity and temperature for modeling indoor CO2 by means of a multilayer perceptron (MLP), aiming to avoid deploying expensive CO2 sensors. Although data collected over six months were considered and advanced statistical features (e.g., kurtosis, skewness) were extracted from data to get as much information as possible to train the model, the performance was poor, and the same authors underlined the need for additional input variables. As a result, the following studies focused on adding new input variables. For example, in [7] the outdoor temperature and humidity--and other different parameters, such as the date and time--were introduced to predict CO2 using random forest, taking into account different training dataset sizes and numbers of trees. In this case, better performance was achieved, but a large dataset covering more than a year was used for training. Unlike the previous studies, the historical data of CO2 and time parameters, such as weekday, hour and minute, were integrated within the study provided by [8]. Two different scenarios were analyzed using an ANN with three hidden layers: the first case considered all input variables, including the time information, and the second case only focused on the historical data of CO2. The results show that the time and date variables do not contribute to improving the accuracy of predictions. Moreover, the chosen approach relies on a large amount of data (corresponding to a time window of 242 days) to train the model. The evaluation was limited to just 10 h of the day after the training time window. A similar scenario considering only CO2 data as the input variable was evaluated in [12]. In this case, the authors took into account CO2 values with a time granularity equal to one hour, bounding the analysis to only working hours. The proposed neural network was trained using the data of a week (from Monday to Friday, excluding the night period), and CO2 values of next Monday and Tuesday were predicted. Although the results proved a good correlation between the true and predicted values using a small amount of data for training the neural network, this study was affected by clear limitations: only a couple of days were considered for the test, thereby restricting the performance overview of the proposedmethod, and a large time granularity (i.e., 1 h) of data was used. A deep analysis regarding the benefits of using CO2 as an input variable was provided by [PERSON] et al. [13]. Three different approaches were considered: the first case included CO2 as the input variable; the second case only took into account humidity and indoor temperature; and the third case partially used CO2 as the input variable to evaluate predictions five time-steps in the future. All these cases correspond to different ways of training the neural network (in this case, MLP). Similarly to in [14], the authors highlighted the importance of collecting CO2 data through sensors to be used in the model training. Despite the use of a small amount of data for training the model and the good prediction accuracy, a limited scenario given by just a week of data was evaluated as in [8; 12]. Furthermore, all the above studies did not consider updating of the models over time. In the literature, some studies included the prediction of CO2 as a part of the forecast of IAQ in general. Unlike the previous works, they also considered different neural network architectures for forecasting IAQ. For example, in [11] the authors estimated and predicted CO2 and particulate matter (PM) 2.5 in some university classrooms through an optimized long short term memory (LSTM) model. Despite the good prediction accuracy, they took into account detailed variables (e.g., indoor NO2, wind speed, wind direction and number of students) which need cutting-edge sensors, making the whole data collection system to predict indoor CO2 expensive.