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Publication
Indoor microclimate monitoring and forecasting: Public Sector building use case
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| 3.66 MB | Adobe PDF |
Advisor(s)
Abstract(s)
This research aims to demonstrate a machine learning (ML) algorithm-based indoor air quality (IAQ) monitoring and forecasting system for a public sector building use case. Such a system has the potential to automate existing heating/ventilation systems, therefore reducing energy consumption. One of Riga Technical University’s campus buildings, equipped with around 128 IAQ sensors, is used as a test bed to create a digital shadow including a comparison of five ML-based data prediction tools. We compare the IAQ data prediction loss using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) error metrics based on real sensor data. Gated Recurrent Unit (GRU) and Kolmogorov–Arnold Networks (KAN) prove to be the most accurate models regarding the prediction error. Also, GRU proved to be the most efficient model regarding the required computation time.
Description
Keywords
Indoor air quality Sensor network Internet of Things Digital shadow Data forecasting Machine learning algorithms
Pedagogical Context
Citation
SUDNIKS, R. [et al.] (2025) - Indoor microclimate monitoring and forecasting: Public Sector building use case. Information. Vol. 16:2, 121. DOI: 10.3390/ info16020121
Publisher
MDPI AG