Modelling temperature in intelligent buildings by means of autoregressive models

One of the main problems of the intelligent buildings is to give comfort to its occupants and to increase the user's performance at a low cost. The excessive demand of electric energy due to heating, ventilating, and air-conditioning (HVAC) systems require temperature forecast and control to make maximum reduction of the electrical energy. The objective of this paper is to investigate in what extent linear autoregressive models with external input (ARX) and autoregressive moving average models with external input (ARMAX), could be used in order to predict the interior air temperature of a building. In particular, the obtained results in the classrooms of the Universidad Autónoma de Querétaro, U.A.Q., México, are shown. Outside air temperature, global solar radiation flux, outside air relative humidity and air velocity were used as the input variables. The obtained results showed that the ARX models give a better prediction of the temperature than the ARMAX models, obtaining the best results with the ARX (2,3,0) with a coefficient of determination of 0.9457 and ARX (2,2,1) with a coefficient of determination of 0.9056.