Simplified Weather-Related Building Energy Disaggregation and Change-Point Regression: Heating and Cooling Energy Use Perspective
End-use consumption provides more detailed information than total consumption and reveals the mechanism of energy flow through a given building. Specifically, for weather-sensitive energy end-uses, it enables the prioritization and selection of heating and cooling areas requiring investigation and a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/12/10/1717 |
| _version_ | 1797474613178925056 |
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| author | Deuk-Woo Kim Ki-Uhn Ahn Hyery Shin Seung-Eon Lee |
| author_facet | Deuk-Woo Kim Ki-Uhn Ahn Hyery Shin Seung-Eon Lee |
| author_sort | Deuk-Woo Kim |
| collection | DOAJ |
| description | End-use consumption provides more detailed information than total consumption and reveals the mechanism of energy flow through a given building. Specifically, for weather-sensitive energy end-uses, it enables the prioritization and selection of heating and cooling areas requiring investigation and actions. One of the major barriers to acquiring such heating and cooling information for small- and medium-sized buildings or low-income households is the high cost related to submetering and maintenance. The end-use data, especially for heating and cooling end-uses, of such-sized buildings are a national blind spot. In this study, to alleviate this measurement cost problem, two weather-sensitive energy disaggregation methods were examined: the simplified weather-related energy disaggregation (SED) and change-point regression (CPR) methods. The first is a nonparametric approach based on heuristics, whereas the second is a parametric approach. A comparative analysis (one-way ANOVA, correlation analysis, and individual comparison) was performed to explore the disaggregation results regarding heating and cooling energy perspectives using a measurement dataset (MEA) from eleven office buildings. The ANOVA results revealed that there was no significant difference between the three groups (SED, CPR, and MEA); rather strong correlation was observed (r > 0.95). Furthermore, an analysis of the building-level comparison showed that the more distinct the seasonal usage in the monthly consumption pattern, the lower the estimation error. Thus, the two approaches appropriately estimated the amount of heating and cooling used compared with the measurement dataset and demonstrated the possibility of mutual complements. |
| first_indexed | 2024-03-09T20:33:35Z |
| format | Article |
| id | doaj.art-f7c24ede906946efa9ccb31835a7fd95 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T20:33:35Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-f7c24ede906946efa9ccb31835a7fd952023-11-23T23:18:50ZengMDPI AGBuildings2075-53092022-10-011210171710.3390/buildings12101717Simplified Weather-Related Building Energy Disaggregation and Change-Point Regression: Heating and Cooling Energy Use PerspectiveDeuk-Woo Kim0Ki-Uhn Ahn1Hyery Shin2Seung-Eon Lee3Department of Building Energy Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, KoreaDepartment of Building Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, KoreaDepartment of Building Energy Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, KoreaDepartment of Building Energy Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, KoreaEnd-use consumption provides more detailed information than total consumption and reveals the mechanism of energy flow through a given building. Specifically, for weather-sensitive energy end-uses, it enables the prioritization and selection of heating and cooling areas requiring investigation and actions. One of the major barriers to acquiring such heating and cooling information for small- and medium-sized buildings or low-income households is the high cost related to submetering and maintenance. The end-use data, especially for heating and cooling end-uses, of such-sized buildings are a national blind spot. In this study, to alleviate this measurement cost problem, two weather-sensitive energy disaggregation methods were examined: the simplified weather-related energy disaggregation (SED) and change-point regression (CPR) methods. The first is a nonparametric approach based on heuristics, whereas the second is a parametric approach. A comparative analysis (one-way ANOVA, correlation analysis, and individual comparison) was performed to explore the disaggregation results regarding heating and cooling energy perspectives using a measurement dataset (MEA) from eleven office buildings. The ANOVA results revealed that there was no significant difference between the three groups (SED, CPR, and MEA); rather strong correlation was observed (r > 0.95). Furthermore, an analysis of the building-level comparison showed that the more distinct the seasonal usage in the monthly consumption pattern, the lower the estimation error. Thus, the two approaches appropriately estimated the amount of heating and cooling used compared with the measurement dataset and demonstrated the possibility of mutual complements.https://www.mdpi.com/2075-5309/12/10/1717energy benchmarkingmonthly energy disaggregationchange point regressionmonthly utility billsmall or medium buildingenergy end-use |
| spellingShingle | Deuk-Woo Kim Ki-Uhn Ahn Hyery Shin Seung-Eon Lee Simplified Weather-Related Building Energy Disaggregation and Change-Point Regression: Heating and Cooling Energy Use Perspective Buildings energy benchmarking monthly energy disaggregation change point regression monthly utility bill small or medium building energy end-use |
| title | Simplified Weather-Related Building Energy Disaggregation and Change-Point Regression: Heating and Cooling Energy Use Perspective |
| title_full | Simplified Weather-Related Building Energy Disaggregation and Change-Point Regression: Heating and Cooling Energy Use Perspective |
| title_fullStr | Simplified Weather-Related Building Energy Disaggregation and Change-Point Regression: Heating and Cooling Energy Use Perspective |
| title_full_unstemmed | Simplified Weather-Related Building Energy Disaggregation and Change-Point Regression: Heating and Cooling Energy Use Perspective |
| title_short | Simplified Weather-Related Building Energy Disaggregation and Change-Point Regression: Heating and Cooling Energy Use Perspective |
| title_sort | simplified weather related building energy disaggregation and change point regression heating and cooling energy use perspective |
| topic | energy benchmarking monthly energy disaggregation change point regression monthly utility bill small or medium building energy end-use |
| url | https://www.mdpi.com/2075-5309/12/10/1717 |
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