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2021 Vol.15, Issue 2 Preview Page

Research Article

Development of a Machine Learning-based Low-rise Residential Building Energy Consumption Prediction Model

머신러닝기반 저층 주거 건물 에너지소요량 예측 모델 개발 - 단독주택, 다세대, 연립주택을 대상으로

Dong-Chul Yoo1
,
Kyung-Soo Kim2
,
Chang-Ho Choi3
,
Sung-Eun Cho4
,
Hyang-In Jang5*
유 동철1
,
김 경수2
,
최 창호3
,
조 성은4
,
장 향인5*
1Senior Researcher, Institute of Green Building and New Technology, Mirae Environment Plan, Seoul, Korea
2Researcher, Institute of Green Building and New Technology, Mirae Environment Plan, Seoul, Korea
3Professor, Department of Architectural Engineering, Kwangwoon University, Seoul, Korea
4Assistant Professor, Faculty of Information Networking for Innovation and Design, Toyo University, Tokyo, Japan
5Director, Institute of Green Building and New Technology, Mirae Environment Plan, Seoul, Korea
1미래환경플랜건축사사무소 건축친환경신기술연구소 책임연구원
2미래환경플랜건축사사무소 건축친환경신기술연구소 선임연구원
3광운대학교 건축공학과 교수
4일본 동양대학교 정보연계학부 조교수
5미래환경플랜건축사사무소 건축친환경신기술연구소 연구소장
*Corresponding Author
30 April 2021. pp. 152-165
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Abstract

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.

Keywords
Machine learning
Low-rise Residential Building
Energy Consumption
Prediction
키워드
머신러닝
저층 주거 건물
에너지소요량
예측
References
1
Kim, J.H., Seong, N.C., Choi, W.C., Choi, K.B. (2018). An Analysis of the Prediction Accuracy of HVAC Fan Energy Consumption According to Artificial Neural Network Variables. Journal of the Architectural Institute of Korea Structure & Construction, 34(11), 73-79.
2
Kim, K.S., Yoo, D.C., Choi, C.H., Jang, H.I. (2020). Development of Energy Variable Definition and Reference Model for Predicting Energy Consumption of Low-rise Residential Buildings. Journal of the Architectural Institute of Korea, 2020, 36, 199-208.
3
Nam, Y.G., Hong, S.K., Cho, S.H., Choi, C.Y. (2019). A Study on the Prediction of Building Energy Consumption Using Deep Learning Technique. Journal of the Korean Society of Mechanical Technology, 21(6), 136-1144.
4
Bae, J.G. (2019). Development of Energy Consumption Prediction Model using Economizer Method in the Office Building with Machine Learning. Master's thesis. Department of Architectural Engineering, Yonsei University, Korea.
5
Hong, S.W. (2020). Prediction for Building and Housing Energy Saving Using Weather Information and Machine Learning. Doctoral thesis, Department of Intelligent Robot Engineering, Hanyang University, Korea.
6
Jung, J.H. (2018). Electricity Load Forecasting with Weather Data for Commercial Buildings based on Machine Learning. Master's thesis. Department of Architectural Engineering, Cheongju University, Korea.
7
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8
Lee, T. G. (2015). Study on Electric Power Consumption in The University Building using Multiple Regression Analysis and Energy Simulation. Master's thesis. Department of Mechanical Engineering, University of Seoul, Korea.
9
No, B.I. (2015). Prediction of Heating Energy Consumption of Apartment Buildings Using Performance Evaluation of Detailed Heating Degree-days. Master's thesis. Department of Architectural Engineering, Chungbuk National University, Korea.
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Park, G.H. (2018). Machine learning for building energy management Based on short/long-term prediction algorithm Performance analysis. Master's thesis. Department of Computer and Information Science, Korea University, Korea.
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Ministry of Land, Infrastructure and Transport (MOLIT). (2018). 2030 Greenhouse Gas Reduction Roadmap Amendment and 2018-2020 emission permit allocation plan confirmed : Ministry of Land, Infrastructure and Transport.
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ASHARAE (2002). ASHRAE Guideline 14 Measurement of Energy, Demand and Water Savings. American Society of Heating, Refrigerating, and Air-Conditioning Engineers, New York, USA.
Information
  • Publisher :Korean Institute of Architectural Sustainable Environment and Building Systems
  • Publisher(Ko) :한국건축친환경설비학회
  • Journal Title :Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
  • Journal Title(Ko) :한국건축친환경설비학회논문집
  • Volume : 15
  • No :2
  • Pages :152-165
  • Received Date : 2020-12-30
  • Revised Date : 2021-02-15
  • Accepted Date : 2021-02-17
  • DOI :https://doi.org/10.22696/jkiaebs.20210013
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