Predicting building's corners hygrothermal behavior by using a Fuzzy inference system combined with clustering and Kalman filter

Abstract The hygroscopic characteristics of building materials can affect thermal gain or losses that are directly associated to energy consumption due to the latent heat transport. Moreover, some specific regions can accumulate humidity on building structures, and some of this regions, known as building corners, are still barely explored due to modelling complexity, high computer run time, numerical divergence, and highly moisture-dependent properties. This article presents an alternative to predict temperature, vapor pressure, and moisture content profiles in specific points where moisture can be easily accumulated, increasing mould growth risks and/or causing structural damage to the building. In order to avoid time-consuming numerical models, this article uses a Takagi–Sugeno fuzzy inference system with a multiple-input, single-output (MISO) structure to predict building corners hygrothermal behavior. Due to the ability of nonlinearity detection, associated with a small number of “if–then” rules with fuzzy antecedents and crisp mathematical functions or linear functions in the resultant part, the fuzzy system was combined with subtractive clustering method and Kalman filter to enhance its performance. The results suggested that the developed Takagi–Sugeno fuzzy model has achieved good accuracy in terms of precision when the results were compared to the analytical model. Moreover, in terms of simulation time, after the tuning and optimization procedures, the prediction of temperature, relative humidity, and vapor pressure on specific nodes are faster than the numerical model.