Development of Simplified Building Energy Prediction Model to Support Policymaking in South Korea—Case Study for Office Buildings
Abstract
:1. Introduction
2. Methodology
2.1. Research Flow
2.2. Reference Building Models
- Residential: Lowvilla, House, Apartment (APT)
- Commercial: Retail, Restaurant, Hotel, Private office, Hospital
- Public: Public office, School, University
2.3. Weather Profile
2.4. Key Variables for Model Inputs
2.5. Model Structure and Regression Method
3. Preliminary Results with Reference Buildings
Regression Results and Sensitivity
4. Multivariate Model Evaluation with Office Building in a Global Warming Scenario
5. Conclusions
- The global coefficients considering global warming with increased outdoor air temperatures were estimated to be 1.27 and 0.9 for cooling and heating, respectively; this means 27% more and 10% less energy consumption in 20 years, according to predictions.
- The prediction performance of the simplified regression model (163 cases) was generally good, with cvRMSE values of 4.3% and 5.5%, while the maximum absolute errors were 26% and 16% for the heating and cooling cases, respectively.
6. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Min | Med | Max | ||
---|---|---|---|---|
Insulation | Envelope | 0.08 | 0.59 | 1.1 |
Windows | 0.5 | 2.25 | 4 | |
SHGC | 0.2 | 0.525 | 0.85 | |
Infiltration | 0.2 | 0.39 | 0.58 | |
Internal heat gains | Lighting | 2 | 8.5 | 15 |
Equipment | 1.5 | 4.75 | 8 | |
People | 0.01 | 0.04 | 0.07 | |
Setpoint temperatures | Heating | 17 | 20.5 | 24 |
Cooling | 29 | 27 | 25 | |
HVAC efficiencies | Heating | 0.5 | 0.745 | 0.99 |
Cooling | 1.5 | 3.25 | 5 |
Min | Med | Max | |||
---|---|---|---|---|---|
Insulation | Envelope | 0.1 | 0.55 | 1 | |
Windows | 0.5 | 2.25 | 4 | ||
SHGC | 0.2 | 0.5 | 0.8 | ||
Infiltration | 0.3 | 1.15 | 2 | Otherwise | |
0.3 | 1.65 | 3 | Restaurant | ||
0.3 | 0.9 | 1.5 | Hotel | ||
0.2 | 1.1 | 2 | Hospital | ||
Internal heat gains | Lighting | 2 | 8 | 14 | |
Equipment | 2 | 8 | 14 | ||
People | 0.2 | 0.6 | 1 | Otherwise | |
0.2 | 0.5 | 0.8 | Hotel | ||
Setpoint temperatures | Heating | 18 | 20.5 | 23 | Otherwise |
18 | 21 | 24 | Restaurant, Hotel | ||
18 | 22 | 26 | Hospital | ||
Cooling | 28 | 26 | 24 | Otherwise | |
28 | 25.5 | 23 | Restaurant, Hotel | ||
HVAC efficiencies | Heating | 0.8 | 0.895 | 0.99 | |
Cooling | 1 | 2.5 | 4 |
Min | Med | Max | ||
---|---|---|---|---|
Insulation | Envelope | 0.1 | 0.55 | 1 |
Windows | 0.5 | 2.25 | 4 | |
SHGC | 0.2 | 0.5 | 0.8 | |
Infiltration | 0.3 | 1.15 | 2 | |
Internal heat gains | Lighting | 2 | 8 | 14 |
Equipment | 2 | 8 | 14 | |
People | 0.2 | 0.6 | 1 | |
Setpoint temperatures | Heating | 18 | 20.5 | 23 |
Cooling | 28 | 26 | 24 | |
HVAC efficiencies | Heating | 0.8 | 0.895 | 0.99 |
Cooling | 1 | 2.5 | 4 |
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Joe, J.; Min, S.; Oh, S.; Jung, B.; Kim, Y.M.; Kim, D.W.; Lee, S.E.; Yi, D.H. Development of Simplified Building Energy Prediction Model to Support Policymaking in South Korea—Case Study for Office Buildings. Sustainability 2022, 14, 6000. https://doi.org/10.3390/su14106000
Joe J, Min S, Oh S, Jung B, Kim YM, Kim DW, Lee SE, Yi DH. Development of Simplified Building Energy Prediction Model to Support Policymaking in South Korea—Case Study for Office Buildings. Sustainability. 2022; 14(10):6000. https://doi.org/10.3390/su14106000
Chicago/Turabian StyleJoe, Jaewan, Seunghyeon Min, Seunghwan Oh, Byungwoo Jung, Yu Min Kim, Deuk Woo Kim, Seung Eon Lee, and Dong Hyuk Yi. 2022. "Development of Simplified Building Energy Prediction Model to Support Policymaking in South Korea—Case Study for Office Buildings" Sustainability 14, no. 10: 6000. https://doi.org/10.3390/su14106000