How accurate is low-cost comfort monitoring? Validating the ComfortCube

Abstract. The reliable measurement of indoor climate parameters is a fundamental prerequisite for evaluating indoor climate and, consequently, user comfort. While professional measurement systems offer high precision, they are often associated with high costs, limited mobility, and restricted scalability in practice. At the same time, classical models for assessing comfort (e.g., the PMV model) focus primarily on thermal parameters and neglect the proven relevance of non-thermal factors such as lighting, acoustics, and CO2 concentration. Against this background, the ComfortCube was developed as a compact, low-cost, and modular device designed to capture indoor environmental conditions in multiple dimensions and to bridge the gap between high-end reference systems and practical field applications. It collects real-time data on illuminance, light color, CO2 concentration, air temperature, relative humidity, sound level, and air pressure using six integrated sensors and an ESP32 microcontroller. The aim of this paper is the empirical validation of the ComfortCube. For this purpose, an experimental field study was conducted in which parallel measurements with two professional reference systems were carried out under real conditions in various indoor spaces. Statistical comparison was performed using bias, standard deviation, root mean square error (RMSE), and Pearson correlation. The results demonstrate that air temperature, relative humidity, barometric pressure, CO2 concentration, and illuminance lie within application-specific tolerance limits. Particularly for air temperature and relative humidity, two key factors in thermal comfort evaluation, only minimal deviations were observed. Sound pressure levels exhibit higher scatter (RMSE: 2.1 dB(A)) and do not reach Class-1 laboratory accuracy but are sufficient for trend analysis and comfort-oriented acoustic assessment. The results confirm that the ComfortCube can be used as a valid instrument for indoor climate field studies, especially in contexts requiring cost-efficient, multidimensional, and practice-oriented data collection. The paper emphasizes the potential of user-centered measurement technology to complement existing methods of assessing indoor environmental quality and to provide a data-based foundation for the further development of comfort-related evaluation models.