Predicting the temperature profile of indoor buildings by using orthonormal basis functions

Orthonormal Basis Functions (OBF) is a structure of dynamic models that have been applied in different classes of dynamic systems. Several works describing the theory and applicability of OBF (Orthonormal Basis Functions) in identification and control can be found in the literature. This work is focused on the problem of finding a Multiple-Input/Single-Output (MISO) OBF model for predicting indoor air temperature and energy consumption. The aim is to analyse an alternative way to do so in relation to well established building energy simulation tools. The model is built in terms of the following variables, that is, the input data, are heating power, outdoor temperature, relative humidity and total solar radiation, and the output data involved is the indoor temperature. The methodology has been tested for the low thermal mass case of the BESTEST model and the output data has been generated by using a building hygrothermal simulation tool. Validation procedures have shown very good agreement in terms of temperature prediction errors between the model and the simulation tool data for both winter and summer periods.