Development of regression equations for predicting energy and hygrothermal performance of buildings

Regression equations can be used for predicting indoor air temperature, relative humidity and energy consumption in an easier and more rapid way than building energy simulation tools. The independent variables, that is, the input data, are heating, ventilation and air conditioning (HVAC) power, outdoor temperature, relative humidity and total solar radiation. The present methodology for obtaining the regression equations is based on defining a couple of linear Multiple-Input/Single-Output (MISO) models, since two main outputs are involved, that is, indoor temperature and relative humidity. The methodology has been tested for the low- and high-thermal mass cases of the BESTest model (cases 600 and 900) and the output data is generated by using a building hygrothermal simulation tool. Validation procedures have shown very good agreement between the regression equations and the simulation tool for both winter and summer periods.