Two processes based on a data-driven model combined with dynamic simulation for demand forecasting and providing energy saving measures

Abstract

In this study, two processes of predictive model combining dynamic simulation and regression model were presented. First, (1) the demand forecasting process defined in this study is a method of predicting energy consumption based on the simulated energy consumption of a target building, and (2) the energy saving process is a method of setting an energy reduction target based on the predicted energy consumption and providing energy saving measures to achieve the target. Specifically, there are two functions based on the regression models which are predicting actual energy consumption by inputting the energy consumption simulation data, and inversely inputting the actual energy consumption and deriving the corresponding simulation conditions. As a result of predicting building energy consumption through the demand forecasting process, the performance in the heating season showed CV-RMSE was 14.622%, and in the cooling season CV-RMSE was 5.877%. In addition, in the case study using the energy saving process, it was possible to derive the heating and cooling set temperature for 5% energy saving as an energy saving method by using weather forecast data.