Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis
Abstract
:1. Introduction
- Energy consumption is significantly affected by household characteristics, such as structure size, geometry, and resident behavior, but there are restrictions on collecting relevant information due to privacy issues.
- Installing end-use energy-consumption measurement systems is difficult because of the prohibitive cost.
2. Literature Review
3. Materials and Methods
3.1. Field Survey Overview
3.2. End-Use Energy-Consumption Classification and Measurement
4. Results
4.1. Field-Survey Results
4.2. End-Use Energy-Consumption Measurement Results
4.3. Development of the End-Use Energy-Consumption Estimation Model
4.3.1. Exploratory Data Analysis
4.3.2. Variable Selection
4.3.3. Development of Estimation Model with Multiple Regression Analysis
4.4. Validation of End-Use Energy-Consumption Estimation Model
- Test-set weather data: June 2016 to May 2017
- HDD (based on 18.3°C): 2624 °C·day
- CDD (based on 24.0°C): 236 °C·day
- Average solar radiation: 149 Wh/m2
- Training-set weather data: May 2017 to April 2018
- HDD (based on 18.3°C): 2830 °C·day
- CDD (based on 24.0°C): 186 °C·day
- Average solar radiation: 142 Wh/m2
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Nomenclature
HDD | Heating degree day (°C·day) |
CDD | Cooling degree day (°C·day) |
AREA | Area for exclusive use (m2) |
PYEAR | Permit year |
ATYPE | Apartment building access type |
LOCATE_v | Apartment-unit vertical location |
LOCATE_h | Apartment-unit horizontal location |
ORIEN | Apartment-unit orientation |
BALCONY | Balcony-extension status |
HEAT_temp | Heating setting temperature (°C) |
HEAT_sub | Use of auxiliary heating equipment |
HEAT_source | Heat-source type |
nAIR | Number of air conditioners mainly used (EA) |
AIR_type | Type of air conditioner mainly used |
AIR_hour | Average daily air-conditioner operating hours |
DHW_wm | Use of hot water in the washing machine |
nDHW | Number of sanitary appliances for hot water (shower, wash basin, and sink; EA) |
LD | Lighting density (W/m2) |
L_hour | Average daily lighting hours (h/day) |
LED | LED use |
V_bath_hour | Average daily bathroom ventilation-fan operating hours (h/day) |
V_kit_hour | Average daily kitchen exhaust-fan operating hours (h/day) |
V_unit | Use of ventilation unit |
nREF | Number of refrigerators (including kimchi refrigerator; EA) |
nTV | Number of TVs (EA) |
nPC | Number of PCs (EA) |
nWP | Number of water purifiers (EA) |
nAP | Number of air cleaners (EA) |
COOK | Cooking-appliance type |
nRES | Number of residents (person) |
nMALE | Number of males (person) |
nFEMALE | Number of females (person) |
nAGE (≥65) | Number of residents aged 65 or older (person) |
nAGE (20–64) | Number of residents aged between 20 and 64 (person) |
nAGE (8–19) | Number of residents aged between 8 and 19 (person) |
nAGE (≤7) | Number of residents aged 7 or younger (person) |
nECONO_act | Number of economically active residents (person) |
nECONO_inact | Number of economically inactive residents (person) |
References
- Korea Energy Economics Institute; Korea Energy Agency. 2017 Energy Consumption Survey; Ministry of Trade, Industry and Energy: Sejong, Korea, 2018.
- Swan, L.G.; Ugursal, V.I. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renew. Sustain. Energy Rev. 2009, 13, 1819–1835. [Google Scholar] [CrossRef]
- Shimoda, Y.; Fujii, T.; Morikawa, T.; Mizuno, M. Development of residential energy end-use simulation model at city scale. In Proceedings of the Eighth International IBPSA Conference, Eindhoven, The Netherlands, 11–14 August 2003; pp. 1201–1208. [Google Scholar]
- Aydinalp-Koksal, M.; Ugursal, V.I. Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector. Appl. Energy 2008, 85, 271–296. [Google Scholar] [CrossRef]
- Ryan, P.; Pavia, M. Australian residential energy end-use—Trends and projections to 2030. In Proceedings of the 2016 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, USA, 21–26 August 2016. [Google Scholar]
- Ren, Z.; Foliente, G.; Chan, W.Y.; Chen, D.; Ambrose, M.; Paevere, P. A model for predicting household end-use energy consumption and greenhouse gas emissions in Australia. Int. J. Sustain. Build. Technol. Urban Dev. 2013, 4, 210–228. [Google Scholar] [CrossRef] [Green Version]
- Min, J.; Hausfather, Z.; Lin, Q.F. A high-resolution statistical model of residential energy end use characteristics for the United States. J. Ind. Ecol. 2010, 14, 791–807. [Google Scholar] [CrossRef]
- Fumo, N.; Biswas, M.A.R. Regression analysis for prediction of residential energy consumption. Renew. Sustain. Energy Rev. 2015, 47, 332–343. [Google Scholar] [CrossRef]
- Li, C. Home Energy Consumption Estimation by End Use and Energy Efficiency Upgrade Recommendations. Master’s Thesis, Nicholas School of the Environment, Duke University, Durham, NC, USA, 2014. [Google Scholar]
- Matsumoto, S. Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data; Working Paper E-98; Tokyo Center for Economic Research: Tokyo, Japan, 2015. [Google Scholar]
- Parti, M.; Parti, C. The total and appliance-specific conditional demand for electricity in the household sector. Bell J. Econ. 1980, 11, 3029–3321. [Google Scholar] [CrossRef]
- Larsen, B.M.; Nesbakken, R. Household electricity end-use consumption: Results from econometric and engineering models. Energy Econ. 2004, 26, 179–200. [Google Scholar] [CrossRef]
- Aydinalp-Koksal, M.; Ugursal, V.I.; Fung, A.S. Modeling of the appliance, lighting, and space-cooling energy consumption in the residential sector using neural network. Appl. Energy 2002, 71, 87–110. [Google Scholar] [CrossRef]
- ISO 12655:2013. Energy Performance of Buildings: Presentation of Measured Energy Use of Buildings; International Organization for Standardization: Geneva, Switzerland, 2013. [Google Scholar]
- ISO 16346:2013. Energy Performance of Buildings: Assessment of All Overall Energy Performance; International Organization for Standardization: Geneva, Switzerland, 2013. [Google Scholar]
- Choi, B.; Jin, H.; Kang, J.; Kim, S.; Lim, J.; Song, S. Measurement and normalization methods of energy consumption by end-use in apartment buildings for providing detailed energy information. J. Korean Inst. Archit. Sustain. Environ. Build. Syst. 2015, 9, 437–447. [Google Scholar]
- Kang, J.; Kim, S.; Jin, H.; Lim, S.; Lim, J.; Song, S. Development of estimation model for end-use energy consumption by usage in apartment building units via conditional demand analysis. J. Korean Inst. Archit. Sustain. Environ. Build. Syst. 2017, 11, 131–141. [Google Scholar]
- Chen, J.; Wang, X.; Steemers, K. A statistical analysis of a residential energy consumption survey study in Hangzhou, China. Energy Build. 2013, 66, 193–202. [Google Scholar] [CrossRef]
- Newsham, G.R.; Donnelly, C.L. A model of residential energy end-use in Canada: Using conditional demand analysis to suggest policy options for community energy planners. Energy Policy 2013, 59, 133–142. [Google Scholar] [CrossRef] [Green Version]
- Lee, K.; Yang, J.; Ryu, U. A study on the estimation model of the amount of the electric energy consumption according to the apartment heating type. J. Korea Inst. Ecol. Archit. Environ. 2010, 10, 57–64. [Google Scholar]
- Bedir, M.; Hasselaar, E.; Itard, L. Determinants of electricity consumption in Dutch dwelling. Energy Build. 2013, 58, 194–207. [Google Scholar] [CrossRef]
- Rea, L.M.; Parker, R.A. Designing and Conducting Survey Research: A Comprehensive Guide, 3rd ed.; Jossey-Bass: San Francisco, CA, USA, 2005; ISBN 978-0787975463. [Google Scholar]
- Ruiz, G.R.; Bandera, C.F. Validation of calibrated energy models: Common errors. Energies 2017, 10, 1587. [Google Scholar] [CrossRef]
- Katipamula, S.; Brambley, M.R. Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part II. HVAC R Res. 2005, 11, 169–187. [Google Scholar] [CrossRef]
- Deshmukh, S.; Glicksman, L.; Norford, L. Case study results: Fault detection in air-handling units in buildings. Adv. Build. Energy Res. 2018, 1756–2201. [Google Scholar] [CrossRef]
Category | Variable | Units | Scale | Range | |
---|---|---|---|---|---|
Building-related characteristics | AREA | m2 | Ratio | 26–137 | |
PYEAR 1 | - | Nominal | (Dummy variable) 0: before 2000, 1: after 2001 | ||
ATYPE | - | Nominal | (Dummy variable) 0: apartment house of staircase type 1: apartment house of corridor access type | ||
LOCATE_v | - | Nominal | (Dummy variable) (0,0) bottom floor, (1,0) middle floor, (0,1) top floor | ||
LOCATE_h | - | Nominal | (Dummy variable) 0: on the sides, 1: in the center | ||
ORIEN | - | Nominal | (Dummy variable) (0,0) southeast or east, (1,0) south, (0,1) southwest or west | ||
BALCONY | - | Nominal | (Dummy variable) 0: not extended, 1: extended | ||
System-related characteristics | HEAT_temp | °C | Ratio | 18–30 | |
HEAT_sub | - | Nominal | (Dummy variable) 0: not used, 1: used | ||
HEAT_source | - | Nominal | (Dummy variable) 0: District heating, 1: City gas (individual heating) | ||
nAIR 2 | EA | Ratio | 1–3 | ||
AIR_type | - | Nominal | (Dummy variable) 0: stand type, 1: wall type | ||
AIR_hour 3 | - | Nominal | (Dummy variable) (0,0) 2 h or less, (1,0) 12 h or less, (0,1) more than 12 h | ||
DHW_wm | - | Nominal | (Dummy variable) 0: not used, 1: used | ||
nDHW | EA | Ratio | 3–8 | ||
LD | W/m2 | Ratio | 2–21 | ||
L_hour | h/day | Ratio | 1–12 | ||
LED | - | Ratio | (Dummy variable) 0: not used, 1: used | ||
V_bath_hour | h/day | Ratio | 0.12–3.5 | ||
V_kit_hour | h/day | Ratio | 0.12–2 | ||
V_unit | - | Ratio | (Dummy variable) 0: not used, 1: used | ||
nREF | EA | Ratio | 1–5 | ||
nTV | EA | Ratio | 1–4 | ||
nPC | EA | Ratio | 0–2 | ||
nWP | EA | Ratio | 0–1 | ||
nAP | EA | Ratio | 0–1 | ||
COOK | - | Nominal | (Dummy variable) 0: electric cooktop, 1: gas cooktop | ||
User-related characteristics | nRES | person | Ratio | 1–5 | |
by gender | nMALE | person | Ratio | 0–4 | |
nFEMALE | person | Ratio | 0–4 | ||
by age | nAGE (≥65) | person | Ratio | 0–3 | |
nAGE (20–64) | person | Ratio | 0–5 | ||
nAGE (8–19) | person | Ratio | 0–3 | ||
nAGE (≤7) | person | Ratio | 0–1 | ||
by economic activity 4 | nECONO_act | person | Ratio | 0–4 | |
nECONO_inact | person | Ratio | 0–4 |
Heating | Cooling | DHW | Lighting | Ventilation | Electric Appliances | Cooking | ||
---|---|---|---|---|---|---|---|---|
Number of effective samples | 42 | 39 | 35 | 54 | 71 | 39 | 37 | |
Average (Ratio) | 6926 (51.9%) | 117 (0.9%) | 1950 (14.6%) | 429 (3.2%) | 32 (0.2%) | 3086 (23.1%) | 816 (6.1%) | |
Max | 14,325 | 292 | 5184 | 1147 | 149 | 4576 | 1646 | |
Min | 1226 | 29 | 386 | 137 | 0 | 2,014 | 312 | |
Percentile | 25th | 3506 | 67 | 1109 | 263 | 14 | 2,487 | 530 |
50th | 6746 | 105 | 1693 | 374 | 22 | 3,064 | 811 | |
75th | 9698 | 149 | 2426 | 581 | 43 | 3,586 | 979 | |
Standard Deviation | 3504 | 68 | 1111 | 217 | 29 | 641 | 332 |
Variable | Heating | Cooling | DHW | |||||
---|---|---|---|---|---|---|---|---|
R | ANOVA | R | ANOVA | R | ANOVA | |||
p-R | Cramér | p-R | Cramér | p-R | Cramér | |||
Building | AREA | 0.620 * | - | 0.177 | - | 0.264 | - | |
PYEAR | - | 0.072 | - | 0.290 | - | 0.065 | ||
ATYPE | - | 0.000 | - | 0.627 | - | 0.283 | ||
0.780 (AREA) | ||||||||
LOCATE_v | - | 0.012 * | - | 0.942 | - | 0.646 | ||
LOCATE_h | - | 0.329 | - | 0.977 | - | 0.586 | ||
ORIEN | - | 0.527 | - | 0.188 | - | 0.322 | ||
BALCONY | - | 0.104 | - | 0.101 | - | 0.455 | ||
System | HEAT_temp | 0.538 * | - | n/a | n/a | n/a | n/a | |
HEAT_sub | - | 0.006 * | n/a | n/a | n/a | n/a | ||
HEAT_source | - | 0.462 | n/a | n/a | n/a | n/a | ||
nAIR | n/a | n/a | 0.373 * | - | n/a | n/a | ||
AIR_type | n/a | n/a | - | 0.170 | n/a | n/a | ||
AIR_hour | n/a | n/a | - | 0.000 * | n/a | n/a | ||
DHW_wm | n/a | n/a | n/a | n/a | - | 0.225 | ||
nDHW | n/a | n/a | n/a | n/a | 0.235 | - | ||
User | nRES | 0.099 | - | 0.221 | - | 0.649 * | - | |
By gender | nMALE | 0.102 | - | 0.197 | - | 0.311 | - | |
−0.142 (n RES) | ||||||||
nFEMALE | 0.051 | - | 0.083 | - | 0.482 | - | ||
0.142 (n RES) | ||||||||
By age | nAGE(≥65) | −0.106 | - | −0.342 * | - | −0.177 | - | |
nAGE(20–64) | 0.114 | - | 0.241 | - | 0.516 | - | ||
0.081 (n RES) | ||||||||
nAGE(8–19) | −0.141 | - | 0.264 | - | 0.149 | - | ||
nAGE(≤7) | 0.153 | - | −0.072 | - | 0.141 | - | ||
by economicactivity | nECONO_act | 0.287 | - | 0.078 | - | 0.529 | - | |
0.129 (n RES) |
Variable | Lighting | Electric Appliances | Cooking | |||||
R | ANOVA | R | ANOVA | R | ANOVA | |||
p-R | Cramér | p-R | Cramér | p-R | Cramér | |||
Building | AREA | 0.535 * | - | 0.414 * | - | 0.123 | - | |
PYEAR | - | 0.017 | - | 0.299 | - | 0.798 | ||
0.387 (AREA) | ||||||||
ATYPE | - | 0.007 | - | 0.033 | - | 0.548 | ||
0.614 (AREA) | 0.630 (AREA) | |||||||
LOCATE_v | - | 0.283 | - | 0.677 | - | 0.733 | ||
LOCATE_h | - | 0.889 | - | 0.601 | - | 0.498 | ||
ORIEN | - | 0.356 | - | 0.417 | - | 0.058 | ||
BALCONY | - | 0.004 | - | 0.122 | - | 0.301 | ||
0.524 (AREA) | ||||||||
System | LD | 0.003 | - | n/a | n/a | n/a | n/a | |
L_hour | 0.463 * | - | n/a | n/a | n/a | n/a | ||
LED | - | 0.390 | n/a | n/a | n/a | n/a | ||
nREF | n/a | n/a | 0.360 * | - | n/a | n/a | ||
nTV | n/a | n/a | 0.389 * | - | n/a | n/a | ||
nPC | n/a | n/a | 0.272 | - | n/a | n/a | ||
nWP | n/a | n/a | 0.294 | - | n/a | n/a | ||
nAP | n/a | n/a | 0.183 | - | n/a | n/a | ||
COOK | n/a | n/a | n/a | n/a | - | 0.827 | ||
User | nRES | 0.535 * | - | 0.191 | - | 0.470 * | - | |
by gender | nMALE | 0.523 | - | 0.268 | - | 0.443 | - | |
0.058 (nRES) | 0.185 (nRES) | |||||||
nFEMALE | 0.321 | - | 0.027 | - | 0.092 | - | ||
−0.049 (nRES) | ||||||||
by age | nAGE(≥65) | −0.178 | - | 0.180 | - | 0.037 | - | |
nAGE(20–64) | 0.361 | - | −0.019 | - | 0.169 | - | ||
−0.058 (nRES) | ||||||||
nAGE(8–19) | 0.378 * | - | 0.136 | - | 0.401 * | - | ||
nAGE(≤7) | −0.186 | - | −0.147 | - | −0.201 | - | ||
by economic activity | nECONO_act | 0.310 | - | 0.020 | - | 0.127 | - | |
−0.310 (nRES) | ||||||||
nECONO_inact | 0.338 | - | 0.272 | - | 0.401 | - | ||
0.109 (nRES) | 0.291 (nRES) |
- | Heating | Cooling | DHW | Lighting | Ventilation | Electric Appliances | Cooking |
---|---|---|---|---|---|---|---|
Equation | (1) | (2) | (3) | (4) | (5) | (6) | (7) |
Adj-R2 | 0.643 | 0.703 | 0.406 | 0.548 | - | 0.407 | 0.429 |
F-ratio (p-value) | 15.767 (0.000) | 18.996 (0.000) | 12.631 (0.000) | 11.694 (0.000) | - | 5.355 (0.001) | 6.000 (0.001) |
Heating 1 | Cooling | DHW | Lighting | Ventilation | Electric Appliances | Cooking | |
---|---|---|---|---|---|---|---|
p-value | 0.011 | 0.506 | 0.575 | 0.475 | 0.521 | 0.473 | 0.566 |
End-use | Validation for 10 Households | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ||
Heating | Measured (kWh/yr) | 7008 | 5407 | 14,044 | 8955 | 6955 | 4674 | 2624 | 7839 | 5805 | 4349 |
Estimated (kWh/yr) | 5937 | 5296 | 12,022 | 6753 | 7529 | 6891 | 3136 | 6993 | 8773 | 4620 | |
Error (%) | −18 | −2 | −17 | −33 | 8 | 32 | 16 | −12 | 34 | 6 | |
Cooling | Measured (kWh/yr) | 274 | 78 | 52 | 80 | 236 | 183 | 32 | 157 | 139 | 77 |
Estimated (kWh/yr) | 308 | 89 | 49 | 77 | 246 | 221 | 24 | 154 | 149 | 90 | |
Error (%) | 11 | 13 | −9 | −4 | 4 | 17 | −31 | −2 | 7 | 14 | |
DHW | Measured (kWh/yr) | 1811 | 1296 | 1296 | 2743 | 3806 | 2228 | 1981 | 3274 | 1148 | 892 |
Estimated (kWh/yr) | 1432 | 1153 | 1397 | 2024 | 4021 | 2876 | 1909 | 3567 | 1216 | 686 | |
Error (%) | −26 | −12 | 7 | −35 | 5 | 23 | −4 | 8 | 6 | −30 | |
Lighting | Measured (kWh/yr) | 576 | 302 | 247 | 698 | 520 | 262 | 241 | 510 | 456 | 311 |
Estimated (kWh/yr) | 633 | 301 | 278 | 726 | 531 | 282 | 222 | 565 | 425 | 333 | |
Error (%) | 9 | 0 | 11 | 4 | 2 | 7 | −8 | 10 | −7 | 6 | |
Ventilation | Measured (kWh/yr) | 50 | 46 | 57 | 85 | 38 | 21 | 18 | 38 | 38 | 13 |
Estimated (kWh/yr) | 54 | 38 | 100 | 97 | 38 | 27 | 20 | 45 | 38 | 21 | |
Error (%) | 6 | −22 | 42 | 13 | 0 | 0 | 13 | 16 | 0 | 36 | |
Electric Appliance | Measured (kWh/yr) | 3782 | 3329 | 2361 | 4189 | 3282 | 2333 | 2647 | 2995 | 2662 | 3523 |
Estimated (kWh/yr) | 3995 | 3418 | 2427 | 4455 | 3352 | 2568 | 2980 | 2687 | 3019 | 3428 | |
Error (%) | 5 | 3 | 3 | 6 | 2 | 9 | 11 | −11 | 12 | −3 | |
Cooking | Measured (kWh/yr) | 773 | 1029 | 795 | 969 | 1463 | 879 | 879 | 879 | 757 | 735 |
Estimated (kWh/yr) | 737 | 963 | 762 | 991 | 1508 | 975 | 922 | 1037 | 705 | 735 | |
Error (%) | −5 | −7 | −4 | 2 | 3 | 10 | 5 | 15 | −19 | 6 |
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Share and Cite
Lee, S.-J.; Kim, Y.-J.; Jin, H.-S.; Kim, S.-I.; Ha, S.-Y.; Song, S.-Y. Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis. Energies 2019, 12, 2327. https://doi.org/10.3390/en12122327
Lee S-J, Kim Y-J, Jin H-S, Kim S-I, Ha S-Y, Song S-Y. Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis. Energies. 2019; 12(12):2327. https://doi.org/10.3390/en12122327
Chicago/Turabian StyleLee, Soo-Jin, You-Jeong Kim, Hye-Sun Jin, Sung-Im Kim, Soo-Yeon Ha, and Seung-Yeong Song. 2019. "Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis" Energies 12, no. 12: 2327. https://doi.org/10.3390/en12122327