Possibilities of using hybrid digital twins to solve problem of predictive energy efficient management in apartment building

This article presents the results of measuring microclimate parameters in a residential apartment in a multi-apartment building in the northern region, focusing on carbon dioxide concentrations as the most sensitive indicator of air quality for the human body. The study examines the dynamics of carbon dioxide concentrations, temperature, relative humidity, and pressure under real-life conditions. This study aims to assess the inertia of the residential microclimate, compare measured carbon dioxide levels with current air quality guidelines, and justify the feasibility of using a digital twin to solve the problem of predictive control of a smart home system. The study methodologically uses a measuring device based on an ESP32-C3 Super Mini microcontroller with AHT20, BMP280, and SCD40 sensors, which generates time series of microclimate parameters. It is shown that during the overnight stay of one person, carbon dioxide concentrations increase monotonically from approximately 1100 cm³/m³ to values above 1800 cm³/m³, exceeding the recommended limits for residential premises. The scientific novelty of this study lies in the experimental substantiation of the fact that the bedroom microclimate can be considered an inertial system, for which the use of predictive control tools for smart home devices based on the predicted dynamics of carbon dioxide is advisable. It is also substantiated that a digital twin integrating sensor data and a room model can proactively detect deteriorating air quality and initiate the most energy-saving control actions.

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