{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T21:54:21Z","timestamp":1787435661896,"version":"build-2736575974"},"reference-count":22,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"TwinAIR","award":["101057779"],"award-info":[{"award-number":["101057779"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Given the significant implications of indoor air pollution for physical and mental health, well-being and productivity, indoor air quality (IAQ) is of critical importance. CO2 is a prevalent indoor air contaminant and represents a key determinant for IAQ characterization. This study collected sensed air pollution and climatic data from a hospital environment in Greece and employed Long Short-Term Memory (LSTM) neural network variants with progressively increased architectural complexity to predict indoor CO2 concentration across future horizons ranging from 15 min up to 180 min. Among the examined variants, the attention-based LSTM exhibited the most consistent performance across the forecasting horizons. Incorporating additional predictors reflecting climatic conditions, air pollution and occupancy status within the hospital settings, the multivariate attention-based LSTM further enhanced its predictive performance with an MAE of 8.9 ppm, 16.7 ppm, 31.2 ppm, 38.9 and 39.5 ppm for 15 min, 30 min, 60 min, 120 min, and 180 min ahead, respectively.<\/jats:p>","DOI":"10.3390\/s25175382","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T12:04:28Z","timestamp":1756814668000},"page":"5382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Short-Term Forecast of Indoor CO2 Using Attention-Based LSTM: A Use Case of a Hospital in Greece"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8662-5591","authenticated-orcid":false,"given":"Christos","family":"Mountzouris","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Patras, 265 04 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1937-5465","authenticated-orcid":false,"given":"Grigorios","family":"Protopsaltis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Patras, 265 04 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7738-1604","authenticated-orcid":false,"given":"John","family":"Gialelis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Patras, 265 04 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5584960","DOI":"10.1155\/2024\/5584960","article-title":"Impact of indoor air quality and multi-domain factors on human productivity and physiological responses: A comprehensive review","volume":"2024","author":"Deng","year":"2024","journal-title":"Indoor Air"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4467","DOI":"10.1038\/s41467-021-24487-w","article-title":"The mortality cost of carbon","volume":"12","author":"Bressler","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"162163","DOI":"10.1016\/j.scitotenv.2023.162163","article-title":"Critical review on emerging health effects associated with the indoor air quality and its sustainable management","volume":"872","author":"Kumar","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10916-016-0667-2","article-title":"Monitoring indoor air quality for enhanced occupational health","volume":"41","author":"Pitarma","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_5","unstructured":"U.S. Environmental Protection Agency (2025, July 08). Why Indoor Air Quality Is Important to Schools, Available online: https:\/\/www.epa.gov\/iaq-schools\/why-indoor-air-quality-important-schools."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"159088","DOI":"10.1016\/j.scitotenv.2022.159088","article-title":"CO2 in indoor environments: From environmental and health risk to potential renewable carbon source","volume":"856","author":"Kraakman","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wallington, T.J., Anderson, J.E., Dolan, R.H., and Winkler, S.L. (2022). Vehicle emissions and urban air quality: 60 years of progress. Atmosphere, 13.","DOI":"10.3390\/atmos13050650"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1016\/j.jaci.2024.08.013","article-title":"Indoor air pollution and airway health","volume":"154","author":"Radbel","year":"2024","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_9","unstructured":"ASHRAE (2022). ASHRAE Standard 62.1-2022: Ventilation and Acceptable Indoor Air Quality, American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41370-024-00694-7","article-title":"Carbon dioxide guidelines for indoor air quality: A review","volume":"34","author":"Mendell","year":"2024","journal-title":"J. Expo. Sci. Environ. Epidemiol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ogundiran, J., Asadi, E., and Gameiro da Silva, M. (2024). A systematic review on the use of AI for energy efficiency and indoor environmental quality in buildings. Sustainability, 16.","DOI":"10.3390\/su16093627"},{"key":"ref_12","unstructured":"European Commission (2025, July 10). Energy Performance of Buildings Directive. Available online: https:\/\/energy.ec.europa.eu\/topics\/energy-efficiency\/energy-performance-buildings\/energy-performance-buildings-directive_en."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111684","DOI":"10.1016\/j.buildenv.2024.111684","article-title":"A virtual sensing approach to enhancing personalized strategies for indoor environmental quality and residential energy management","volume":"261","author":"Karatzas","year":"2024","journal-title":"Build. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1109\/TSG.2018.2875727","article-title":"Energy optimization of HVAC systems in commercial buildings considering indoor air quality management","volume":"10","author":"Yu","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107409","DOI":"10.1016\/j.buildenv.2020.107409","article-title":"Forecasting office indoor CO2 concentration using machine learning with a one-year dataset","volume":"187","author":"Kallio","year":"2021","journal-title":"Build. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9404807","DOI":"10.1155\/2022\/9404807","article-title":"Machine learning-based CO2 prediction for office room: A pilot study","volume":"2022","author":"Kapoor","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Al-Ahmed, S.A., Shakir, M.Z., and Olszewska, J.I. (2023). LSTM-based IoT-enabled CO2 steady-state forecasting for indoor air quality monitoring. Electronics, 12.","DOI":"10.3390\/electronics12010107"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ahn, K.U., Kim, D.-W., Cho, K., Cho, D., Cho, H.M., and Chae, C.-U. (2022). Hybrid model for forecasting indoor CO2 concentration. Buildings, 12.","DOI":"10.3390\/buildings12101540"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Garcia-Pinilla, P., Jurio, A., and Paternain, D. (2025). A comparative study of CO2 forecasting strategies in school classrooms: A step toward improving indoor air quality. Sensors, 25.","DOI":"10.3390\/s25072173"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108164","DOI":"10.1016\/j.buildenv.2021.108164","article-title":"Learning-based CO2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation","volume":"205","author":"Taheri","year":"2021","journal-title":"Build. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Athanasakis, E., Pantelidou, K., Siopis, N., Bizopoulos, P., Lalas, A., Votis, K., and Tzovaras, D. (2024, January 23\u201324). Towards safer indoor spaces: A machine learning based CO2 forecasting approach for smart systems. Proceedings of the 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), Madrid, Spain.","DOI":"10.1109\/EAIS58494.2024.10570031"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Segala, G., Doriguzzi-Corin, R., Peroni, C., Gazzini, T., and Siracusa, D. (2021). A practical and adaptive approach to predicting indoor CO2. Appl. Sci., 11.","DOI":"10.3390\/app112210771"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:36:55Z","timestamp":1760035015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":22,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25175382"],"URL":"https:\/\/doi.org\/10.3390\/s25175382","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]}}}