# A Practical and Adaptive Approach to Predicting Indoor CO2
## 1 Introduction  
Due to the recent COVID-19 pandemic, indoor air quality (IAQ) has drawn particular attention worldwide, highlighting the importance of guaranteeing safe and comfort conditions to people. One of the main pollutants that most impacts IAQ is carbon dioxide (CO2), which is highly correlated with human presence. Medical and scientific studies have underlined how high levels of CO2 not only affect the cognition [1] and well-being of people, significantly reducing comfort perception within an enclosed environment, but could contribute to COVID-19 infection [2]. Although National and European regulations outline specific levels to keep--e.g., the Joint Research Center (JRC) of the European Commission [3] defines the limit of 1000 ppm as the threshold before the indoor comfort starts to deteriorate significantly--keeping the CO2 level as low as possible is fundamental from a pandemic point of view [2]. In this regard, the deployment of a data collection system to monitor indoor CO2 and IAQ represents a priority, especially in crowded environments such as tertiary buildings (i.e., retail stores, supermarkets and bank branches). A common approach to this consists of smart Internet of Things (IoT) sensors sensing the environment and communicating (through a wide range of possible protocols, e.g., Modbus [4], Wi-Fi [5] and EnOcean [6]) with an edge device which collects and processes the data. Moreover, in complex architectures the edge node could also play the role of gateway towards the network infrastructure. A typical scenario is that of retail stores, depicted in Figure 1, where managers aim toguarantee the best possible environmental conditions within their sites to make customers feel comfortable, hence encouraging them to shop. Although the monitoring of real-time CO2 through smart IoT sensors can give important benefits in this direction, such an approach does not provide any information on the future behavior of CO2. This is a major limitation, as forecasting CO2 is crucial for applying preventive actions, e.g., by means of intelligent systems capable of automatically regulating HVAC devices in advance. Thanks to the increasing interest for IAQ, over the last decade a number of studies regarding the forecast of indoor CO2 have been published [7; 8; 9; 10; 11; 12; 13; 14]. The proposed solutions mainly focus on maximizing the CO2 prediction accuracy by means of different artificial intelligence (AI) techniques. Despite the promising results, a pure performance-oriented approach (i.e., which mainly focuses on prediction accuracy) might limit the applicability of such solutions in real-world scenarios. Indeed, the maximization of the performance is typically achieved by using a significant amount of data collected over a long time period [7; 8; 9; 10] or by leveraging on complex input variables from expensive cutting-edge sensors [9; 11]. In this regard, such a scenario requires data collection for many days, even months, for training the artificial intelligence models. As a result, managers of retail stores have to wait a long time before the full deployment of the system, making it unattractive from a business point of view. Moreover, to the best of our knowledge, none of the state-of-the-art solutions tackle the challenges of periodically updating the model to deal with the changes of the monitored environment (e.g., unusual behaviors of CO2 due to changes in human activity). In this work, we propose a practical approach for predicting indoor CO2 using a limited window of indoor environmental data (i.e., temperature, humidity and CO2) collected over a short time frame to train neural network models. It achieves comparable accuracy of predictions with state-of-the-art solutions. The designed approach not only enables the CO2 predictions to be made very shortly after the beginning of the data collection (i.e., after just one day), but in the first period of the system's deployment, the model can progressively learn new features by increasing the window of data day by day.