Hybrid Model for Forecasting Indoor CO2 Concentration
Auteur(s): |
Ki Uhn Ahn
Deuk-Woo Kim Kyungjoo Cho Dongwoo Cho Hyun Mi Cho Chang-U Chae |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 20 septembre 2022, n. 10, v. 12 |
Page(s): | 1540 |
DOI: | 10.3390/buildings12101540 |
Abstrait: |
Indoor CO2 concentration is considered a metric of indoor air quality that affects the health of occupants. In this study, a hybrid model was developed for forecasting the varying indoor CO2 concentration levels in a residential apartment unit in the presence of occupants by controlling the ventilation rates of a heat recovery ventilator. In this model, the mass balance equation for a single zone as a white-box model was combined with a Bayesian neural network (BNN) as a black box model. During the learning process of the hybrid model, the BNN estimated an aggregated unknown ventilation rate and transferred the estimation to the mass-balance equation. A parametric study was conducted by changing the prediction horizons of the hybrid model from 5 to 15 min, and the forecasting performance of the hybrid model was compared with the stand-alone mass balance equation. The hybrid model showed better forecasting performance than that of the mass balance equation on the experimental dataset for a living room and bedroom. The average MBE and CVRMSE of the hybrid model for the prediction horizon of 15 min were 0.65% and 5.23%, respectively, whereas those of the mass balance equation were 0.99% and 9.30%, respectively. |
Copyright: | © 2022 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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10700040 - Publié(e) le:
11.12.2022 - Modifié(e) le:
10.05.2023