A Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>
COVID-19 has underlined the importance of monitoring indoor air quality (IAQ) to guarantee safe conditions in enclosed environments. Due to its strict correlation with human presence, carbon dioxide (CO<sub>2</sub>) represents one of the pollutants that most affects environmental health....
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/9b22b0dbbb8e49e79e056b6e46d28a0e |
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| Sumario: | COVID-19 has underlined the importance of monitoring indoor air quality (IAQ) to guarantee safe conditions in enclosed environments. Due to its strict correlation with human presence, carbon dioxide (CO<sub>2</sub>) represents one of the pollutants that most affects environmental health. Therefore, forecasting future indoor CO<sub>2</sub> plays a central role in taking preventive measures to keep CO<sub>2</sub> level as low as possible. Unlike other research that aims to maximize the prediction accuracy, typically using data collected over many days, in this work we propose a practical approach for predicting indoor CO<sub>2</sub> using a limited window of recent environmental data (i.e., temperature; humidity; CO<sub>2</sub> of, e.g., a room, office or shop) for training neural network models, without the need for any kind of model pre-training. After just a week of data collection, the error of predictions was around 15 parts per million (ppm), which should enable the system to regulate heating, ventilation and air conditioning (HVAC) systems accurately. After a month of data we reduced the error to about 10 ppm, thereby achieving a high prediction accuracy in a short time from the beginning of the data collection. Once the desired mobile window size is reached, the model can be continuously updated by sliding the window over time, in order to guarantee long-term performance. |
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