Clustering-based probability distribution model for monthly residential building electricity consumption analysis
Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among househo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xu, Jieyan [verfasserIn] Kang, Xuyuan [verfasserIn] Chen, Zheng [verfasserIn] Yan, Da [verfasserIn] Guo, Siyue [verfasserIn] Jin, Yuan [verfasserIn] Hao, Tianyi [verfasserIn] Jia, Rongda [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Building simulation - Beijing : Tsinghua Press, 2008, 14(2020), 1 vom: 26. Sept., Seite 149-164 |
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| Übergeordnetes Werk: |
volume:14 ; year:2020 ; number:1 ; day:26 ; month:09 ; pages:149-164 |
| Links: |
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| DOI / URN: |
10.1007/s12273-020-0710-6 |
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| Katalog-ID: |
SPR042132983 |
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| 520 | |a Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features. | ||
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| 650 | 4 | |a electricity consumption |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kang, Xuyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Zheng |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Da |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Siyue |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Hao, Tianyi |e verfasserin |4 aut | |
| 700 | 1 | |a Jia, Rongda |e verfasserin |4 aut | |
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10.1007/s12273-020-0710-6 doi (DE-627)SPR042132983 (SPR)s12273-020-0710-6-e DE-627 ger DE-627 rakwb eng 620 ASE Xu, Jieyan verfasserin aut Clustering-based probability distribution model for monthly residential building electricity consumption analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features. cluster analysis (dpeaa)DE-He213 probability distribution (dpeaa)DE-He213 residential building (dpeaa)DE-He213 electricity consumption (dpeaa)DE-He213 Kang, Xuyuan verfasserin aut Chen, Zheng verfasserin aut Yan, Da verfasserin aut Guo, Siyue verfasserin aut Jin, Yuan verfasserin aut Hao, Tianyi verfasserin aut Jia, Rongda verfasserin aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 14(2020), 1 vom: 26. Sept., Seite 149-164 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:14 year:2020 number:1 day:26 month:09 pages:149-164 https://dx.doi.org/10.1007/s12273-020-0710-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 1 26 09 149-164 |
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10.1007/s12273-020-0710-6 doi (DE-627)SPR042132983 (SPR)s12273-020-0710-6-e DE-627 ger DE-627 rakwb eng 620 ASE Xu, Jieyan verfasserin aut Clustering-based probability distribution model for monthly residential building electricity consumption analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features. cluster analysis (dpeaa)DE-He213 probability distribution (dpeaa)DE-He213 residential building (dpeaa)DE-He213 electricity consumption (dpeaa)DE-He213 Kang, Xuyuan verfasserin aut Chen, Zheng verfasserin aut Yan, Da verfasserin aut Guo, Siyue verfasserin aut Jin, Yuan verfasserin aut Hao, Tianyi verfasserin aut Jia, Rongda verfasserin aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 14(2020), 1 vom: 26. Sept., Seite 149-164 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:14 year:2020 number:1 day:26 month:09 pages:149-164 https://dx.doi.org/10.1007/s12273-020-0710-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 1 26 09 149-164 |
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10.1007/s12273-020-0710-6 doi (DE-627)SPR042132983 (SPR)s12273-020-0710-6-e DE-627 ger DE-627 rakwb eng 620 ASE Xu, Jieyan verfasserin aut Clustering-based probability distribution model for monthly residential building electricity consumption analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features. cluster analysis (dpeaa)DE-He213 probability distribution (dpeaa)DE-He213 residential building (dpeaa)DE-He213 electricity consumption (dpeaa)DE-He213 Kang, Xuyuan verfasserin aut Chen, Zheng verfasserin aut Yan, Da verfasserin aut Guo, Siyue verfasserin aut Jin, Yuan verfasserin aut Hao, Tianyi verfasserin aut Jia, Rongda verfasserin aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 14(2020), 1 vom: 26. Sept., Seite 149-164 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:14 year:2020 number:1 day:26 month:09 pages:149-164 https://dx.doi.org/10.1007/s12273-020-0710-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 1 26 09 149-164 |
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10.1007/s12273-020-0710-6 doi (DE-627)SPR042132983 (SPR)s12273-020-0710-6-e DE-627 ger DE-627 rakwb eng 620 ASE Xu, Jieyan verfasserin aut Clustering-based probability distribution model for monthly residential building electricity consumption analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features. cluster analysis (dpeaa)DE-He213 probability distribution (dpeaa)DE-He213 residential building (dpeaa)DE-He213 electricity consumption (dpeaa)DE-He213 Kang, Xuyuan verfasserin aut Chen, Zheng verfasserin aut Yan, Da verfasserin aut Guo, Siyue verfasserin aut Jin, Yuan verfasserin aut Hao, Tianyi verfasserin aut Jia, Rongda verfasserin aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 14(2020), 1 vom: 26. Sept., Seite 149-164 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:14 year:2020 number:1 day:26 month:09 pages:149-164 https://dx.doi.org/10.1007/s12273-020-0710-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 1 26 09 149-164 |
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10.1007/s12273-020-0710-6 doi (DE-627)SPR042132983 (SPR)s12273-020-0710-6-e DE-627 ger DE-627 rakwb eng 620 ASE Xu, Jieyan verfasserin aut Clustering-based probability distribution model for monthly residential building electricity consumption analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features. cluster analysis (dpeaa)DE-He213 probability distribution (dpeaa)DE-He213 residential building (dpeaa)DE-He213 electricity consumption (dpeaa)DE-He213 Kang, Xuyuan verfasserin aut Chen, Zheng verfasserin aut Yan, Da verfasserin aut Guo, Siyue verfasserin aut Jin, Yuan verfasserin aut Hao, Tianyi verfasserin aut Jia, Rongda verfasserin aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 14(2020), 1 vom: 26. Sept., Seite 149-164 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:14 year:2020 number:1 day:26 month:09 pages:149-164 https://dx.doi.org/10.1007/s12273-020-0710-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 1 26 09 149-164 |
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Enthalten in Building simulation 14(2020), 1 vom: 26. Sept., Seite 149-164 volume:14 year:2020 number:1 day:26 month:09 pages:149-164 |
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Xu, Jieyan @@aut@@ Kang, Xuyuan @@aut@@ Chen, Zheng @@aut@@ Yan, Da @@aut@@ Guo, Siyue @@aut@@ Jin, Yuan @@aut@@ Hao, Tianyi @@aut@@ Jia, Rongda @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR042132983</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111120649.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201126s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12273-020-0710-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR042132983</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12273-020-0710-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xu, Jieyan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Clustering-based probability distribution model for monthly residential building electricity consumption analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. 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Xu, Jieyan |
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620 ASE Clustering-based probability distribution model for monthly residential building electricity consumption analysis cluster analysis (dpeaa)DE-He213 probability distribution (dpeaa)DE-He213 residential building (dpeaa)DE-He213 electricity consumption (dpeaa)DE-He213 |
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clustering-based probability distribution model for monthly residential building electricity consumption analysis |
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Clustering-based probability distribution model for monthly residential building electricity consumption analysis |
| abstract |
Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features. |
| abstractGer |
Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features. |
| abstract_unstemmed |
Abstract Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features. |
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| container_issue |
1 |
| title_short |
Clustering-based probability distribution model for monthly residential building electricity consumption analysis |
| url |
https://dx.doi.org/10.1007/s12273-020-0710-6 |
| remote_bool |
true |
| author2 |
Kang, Xuyuan Chen, Zheng Yan, Da Guo, Siyue Jin, Yuan Hao, Tianyi Jia, Rongda |
| author2Str |
Kang, Xuyuan Chen, Zheng Yan, Da Guo, Siyue Jin, Yuan Hao, Tianyi Jia, Rongda |
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| doi_str |
10.1007/s12273-020-0710-6 |
| up_date |
2024-07-04T00:57:31.505Z |
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| score |
7.398222 |