Indoor Microclimate Monitoring and Forecasting: Public Sector Building Use Case

Information 2025
Ruslans Sudņiks, Arturs Ziemelis, Agris Ņikitenko, Vasco N. G. J. Soares, Andis Supe

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.

Atslēgas vārdi
indoor air quality; sensor network; Internet of Things; digital shadow; data forecasting; machine learning algorithms
DOI
10.3390/info16020121
Hipersaite
https://www.mdpi.com/2078-2489/16/2/121

Sudņiks, R., Ziemelis, A., Ņikitenko, A., Soares, V., Supe, A. Indoor Microclimate Monitoring and Forecasting: Public Sector Building Use Case. Information, 2025, Vol. 16, No. 2, Article number 121. e-ISSN 2078-2489. Pieejams: doi:10.3390/info16020121

Publikācijas valoda
English (en)