Cooling Energy Demand Evaluation by Meansof Regression Models Obtained From Dynamic Simulations

Abstract

The forecast of the energy heating/cooling demand would be a good indicator for the choice between different conception solutions according to the building characteristics and the local climate. A previous study (Catalina T. et al 2008) was focused on the estimation of heating demand. It is now presented a cooling demand evaluation study. In the early stages of a project, parametric studies have to be done to find an optimum solution among a large number of alternatives. To find a compromise between simple and complex methods of evaluating the cooling energy demand we have proposed to use energy regression models that can approximate with accuracy the results from the model to the data obtained from simulations. The regression energy equations were found to be a good way to quickly estimate the building cooling demand. Among the input data of these regression models it is mentioned the building morphology, sol-air temperature, thermal insulation level, windows U-value and windows surface