| 논문명 |
머신러닝기반 저층 주거 건물 에너지소요량 예측 모델 개발 - 단독주택, 다세대, 연립주택을 대상으로 / Development of a Machine Learning-based Low-rise Residential Building Energy Consumption Prediction Mode |
| 저자명 |
유동철(Yoo, Dong-Chul) ; 김경수(Kim, Kyung-Soo) ; 최창호(Choi, Chang-Ho) ; 조성은(Cho, Sung-Eun) ; 장향인(Jang, Hyang-In) |
| 수록사항 |
한국건축친환경설비학회 논문집, Vol.15 No.2 (2021-04) |
| 주제어 |
머신러닝; 저층 주거 건물; 에너지소요량; 예측 ; Machine learning; Low-rise Residential Building; Energy Consumption; Prediction |
| 요약2 |
The purpose of this study is to develop a prediction model that can evaluate energy consumption before and after remodeling through a reference model for low-rise residential buildings for which energy simulation evaluation is difficult due to its aging. Specifically, a prediction model to evaluate various building elements before remodeling and a model to predict savings due to the application of energy-saving technology were developed. For the objective, the energy simulation analysis of a building was performed using DesignBuilder per reference area of a Detached house, Multi-family house, and Row House. In addition, the significance of machine learning was compared and analyzed by using R2 , MSE, RMSE, CVRMSE indicators and Python’s linear regression, random forest, and neural network. As a result of this analysis, both the model to evaluate the status before remodeling and the model to evaluate the reduction rate according to the energy-saving technology after remodeling showed a high determination coefficient of 0.9 or more for the neuron network. The CVRMSE was analyzed as low as 15% or less. As this is less than the index used in the M&V evaluation presented in the ASHRAE Guideline 14, it was verified that there is a statistical significance. Therefore, this aims to contribute to the basic data and green remodeling promotion project in the energy performance improvement project for the old, private low-rise residential buildings and also in the energy-saving evaluation for buildings. |
