| Title |
Development of a Profiling System for Energy Performance Assessment of Existing Buildings |
| Authors |
Ahn, Ki Uhn ; Kim, Young Min ; Kim, Yong Se ; Yoon, Seong Hwan ; Shin, Han Sol ; Park, Cheol Soo |
| DOI |
http://dx.doi.org/10.5659/JAIK_SC.2016.32.12.77 |
| Keywords |
Building Energy Performance Assessment ; Profiling System ; Artificial Neural Network ; EnergyPlus |
| Abstract |
The building sector contributes to about 40% of total energy consumption in South Korea. In particular, existing buildings older than 15 years account for 75% of the energy consumption by the entire building sector in South Korea. When assessing energy performance of existing buildings by the use of dynamic simulation tools, there are a variety of barriers, e.g. cost, time, expertise, lack of building information, etc. In this study, the authors developed a building energy profiling system that provides quick and easy energy performance assessment of existing buildings. The building energy profiling system is based on a number of EnergyPlus simulation runs and Artificial Neural Network models. For the ANN models, a series of EnergyPlus pre-simulations were sampled by a Monte Carlo technique. Though the profiling system requires minimalistic inputs, it can provide information on (1) energy performance level of a given building, (2) energy benchmarking against peer buildings, and (3) quantification of energy conservation measures. |