Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data
Many smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/12/3189 |
| _version_ | 1797564762408615936 |
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| author | Sukjoon Oh Chul Kim Joonghyeok Heo Sung Lok Do Kee Han Kim |
| author_facet | Sukjoon Oh Chul Kim Joonghyeok Heo Sung Lok Do Kee Han Kim |
| author_sort | Sukjoon Oh |
| collection | DOAJ |
| description | Many smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems for natural gas and water monitoring to be included in SMs. In this study, we analyze heating energy use data obtained from SMs for short-term monitoring and annual predictions using change-point models for the coefficient checking method. It was found that 9-month periods were required to search the best short-term heating energy monitoring periods when non-weather-related and weather-related heating loads and heating change-point temperatures are considered. In addition, the 9-month to 11-month periods were needed for the analysis to apply to other case study residences in the same high-rise apartment. For the accurate annual heating prediction, 11-month periods were necessary. Finally, the results from the heating performance analysis of this study were compared with the cooling performance analysis from a previous study. This study found that the coefficient checking method is a simple and easy-to-interpret approach to analyze interval heating energy use in multi-family residential buildings. It was also found that the period of short-term energy monitoring should be carefully selected to effectively collect targeted heating and cooling data for an energy audit or annual prediction. |
| first_indexed | 2024-03-10T19:02:30Z |
| format | Article |
| id | doaj.art-b13d67080ffd48e484d0696a5cdfc7c6 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T19:02:30Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b13d67080ffd48e484d0696a5cdfc7c62023-11-20T04:21:34ZengMDPI AGEnergies1996-10732020-06-011312318910.3390/en13123189Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption DataSukjoon Oh0Chul Kim1Joonghyeok Heo2Sung Lok Do3Kee Han Kim4CAES Energy Efficiency Research Institute, Mechanical and Biomedical Engineering, Boise State University, Boise, ID 83725, USADepartment of Architecture, Texas A&M University, College Station, TX 77840, USADepartment of Geosciences, University of Texas-Permian Basin, Odessa, TX 79762, USADepartment of Building and Plant Engineering, Hanbat National University, Daejeon 34158, KoreaDepartment of Architectural Engineering, University of Ulsan, Ulsan 44610, KoreaMany smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems for natural gas and water monitoring to be included in SMs. In this study, we analyze heating energy use data obtained from SMs for short-term monitoring and annual predictions using change-point models for the coefficient checking method. It was found that 9-month periods were required to search the best short-term heating energy monitoring periods when non-weather-related and weather-related heating loads and heating change-point temperatures are considered. In addition, the 9-month to 11-month periods were needed for the analysis to apply to other case study residences in the same high-rise apartment. For the accurate annual heating prediction, 11-month periods were necessary. Finally, the results from the heating performance analysis of this study were compared with the cooling performance analysis from a previous study. This study found that the coefficient checking method is a simple and easy-to-interpret approach to analyze interval heating energy use in multi-family residential buildings. It was also found that the period of short-term energy monitoring should be carefully selected to effectively collect targeted heating and cooling data for an energy audit or annual prediction.https://www.mdpi.com/1996-1073/13/12/3189heating energy useinterval datashort-term monitoringannual prediction |
| spellingShingle | Sukjoon Oh Chul Kim Joonghyeok Heo Sung Lok Do Kee Han Kim Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data Energies heating energy use interval data short-term monitoring annual prediction |
| title | Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data |
| title_full | Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data |
| title_fullStr | Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data |
| title_full_unstemmed | Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data |
| title_short | Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data |
| title_sort | heating performance analysis for short term energy monitoring and prediction using multi family residential energy consumption data |
| topic | heating energy use interval data short-term monitoring annual prediction |
| url | https://www.mdpi.com/1996-1073/13/12/3189 |
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