Multiple regression model for fast prediction of the heating energy demand

Nowadays, heating energy demand has become a significant estimator used during the design stage of any new building. In this paper we are proposing a model to predict the heating energy demand, based on the main factors that influence a building's heat consumption. It was found out that these factors are: the building global heat loss coefficient (G), the south equivalent surface (SES) and the difference between the indoor set point temperature and the sol-air temperature. In the second part of this paper, multiple dynamic simulations were carried out in order to determine the values of the inputs and output data of the future prediction model. Using the obtained database, a multiple regression prediction model was further used to develop the prediction model. In the last part of this paper the model results was validated with the measured data from 17 blocks of flats. Moreover, in this article it is also shown the comparison with the results calculated using the building's energy certification methodology. A detailed error analysis showed that the model presents a very good accuracy (correlation coefficient of 0.987). In conclusion, the proposed model presents the following characteristics: three inputs and one output, simplicity, large applicability, good match with the simulations and with the energy certification calculations, human behavior correction. (c) 2012 Elsevier B.V. All rights reserved.