LAUSR

Abstract Many retrofit projects are being carried out in existing buildings to reduce energy consumption. However, the energy consumptions before and after retrofit need to be known in order to evaluate such retrofit projects. Even though the energy consumption after retrofit can be determined through measurement, the energy consumption before retrofit cannot be known. This study is to more easily estimate energy usage prior to the retrofit. Generally, dynamic simulation or regression model should be used to estimate the energy consumption of buildings before retrofit. However, existing regression models have no way to calibrate the model if it is inaccurate. In this paper, we use a clustering technique to improve the accuracy of the regression model. The estimation of energy consumption before retrofit is referred to as “baseline model” and the inverse model is used to create this baseline model. The inverse model is created through monthly data, daily data, and other similar data. In this study, the inverse model was created through daily data and the baseline model was derived from it. The conventional change-point Model and the cluster inverse model presented in this paper were compared and evaluated with the criteria presented through M&V (Measurement and Verification). The results suggest that the cluster inverse model which reflects the characteristics of data is more appropriate when the baseline model is derived from daily data.