Heating and cooling energy consumption prediction model for high-rise apartment buildings considering design parameters

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As lots of the total energy was used by buildings, the number of residential buildings has dramatically increased in South Korea. Thus, it is imperative to pay more attention to energy consumption by residential buildings. In addition, it is important to predict energy consumption in residential buildings accurately. While several studies have currently focused on the data-driven method to predict energy consumption, it requires much information for multivariate data. The present study developed a predictive model for energy consumption for residential buildings by using the statistical method. Using the response surface methodology, the relationships between design factors, and heating and cooling energy use in residential buildings were outlined. The response values were calculated by using the simplified geometries in the energy simulation tool. The relationship has confirmed the dependencies of the energy consumption on various design variables of envelope systems in residential buildings in that the predictive model for the heating and cooling energy consumption was developed. The developed model was compared with the data obtained in the apartment buildings in two cities in South Korea. As a result, a coefficient of variation of the root mean squared error (Cv (RMSE)) was ranged from - 0.3% to 15% and all the comparisons were within the acceptable range. Moreover, heating and cooling energy consumption was predicted by varying the values of design variables such as thermal transmittance, solar heat gain coefficient (SHGC), and air infiltration rates. Among the variables, the largest heating energy was required as with the increase in the air infiltration rates, while the largest cooling energy was consumed as the SHGC was increased for both apartment buildings. Moreover, the increase in thermal transmittance values resulted in about 27% - 29% of the increase in the heating energy consumption. For cooling, 8% to 26% of the energy consumption was decreased when the thermal transmittance was increased. As can be shown, the developed model can offer a rapid energy prediction for apartment buildings with simple information on design variables. Furthermore, it can easily figure out the most important design factor to make a more energy-efficient residential building design.