Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods

Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of building...
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Gespeichert in:

Autor*in:	Do, Huyen [verfasserIn] Cetin, Kristen S.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Energy consumption Residential buildings Energy prediction Energy datasets Inverse modeling

Anmerkung:	© Springer International Publishing AG, part of Springer Nature 2018

Übergeordnetes Werk:	Enthalten in: Current sustainable renewable energy reports - Cham : Springer Internat. Publ., 2014, 5(2018), 1 vom: 25. Jan., Seite 76-85
Übergeordnetes Werk:	volume:5 ; year:2018 ; number:1 ; day:25 ; month:01 ; pages:76-85

Links:	Volltext

DOI / URN:	10.1007/s40518-018-0099-3

Katalog-ID:	SPR03656852X

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520			\|a Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. Summary The literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings.
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allfields	10.1007/s40518-018-0099-3 doi (DE-627)SPR03656852X (SPR)s40518-018-0099-3-e DE-627 ger DE-627 rakwb eng Do, Huyen verfasserin aut Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. Summary The literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings. Energy consumption (dpeaa)DE-He213 Residential buildings (dpeaa)DE-He213 Energy prediction (dpeaa)DE-He213 Energy datasets (dpeaa)DE-He213 Inverse modeling (dpeaa)DE-He213 Cetin, Kristen S. aut Enthalten in Current sustainable renewable energy reports Cham : Springer Internat. Publ., 2014 5(2018), 1 vom: 25. Jan., Seite 76-85 (DE-627)780378741 (DE-600)2760353-2 2196-3010 nnns volume:5 year:2018 number:1 day:25 month:01 pages:76-85 https://dx.doi.org/10.1007/s40518-018-0099-3 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2018 1 25 01 76-85
spelling	10.1007/s40518-018-0099-3 doi (DE-627)SPR03656852X (SPR)s40518-018-0099-3-e DE-627 ger DE-627 rakwb eng Do, Huyen verfasserin aut Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. Summary The literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings. Energy consumption (dpeaa)DE-He213 Residential buildings (dpeaa)DE-He213 Energy prediction (dpeaa)DE-He213 Energy datasets (dpeaa)DE-He213 Inverse modeling (dpeaa)DE-He213 Cetin, Kristen S. aut Enthalten in Current sustainable renewable energy reports Cham : Springer Internat. Publ., 2014 5(2018), 1 vom: 25. Jan., Seite 76-85 (DE-627)780378741 (DE-600)2760353-2 2196-3010 nnns volume:5 year:2018 number:1 day:25 month:01 pages:76-85 https://dx.doi.org/10.1007/s40518-018-0099-3 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2018 1 25 01 76-85
allfields_unstemmed	10.1007/s40518-018-0099-3 doi (DE-627)SPR03656852X (SPR)s40518-018-0099-3-e DE-627 ger DE-627 rakwb eng Do, Huyen verfasserin aut Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. Summary The literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings. Energy consumption (dpeaa)DE-He213 Residential buildings (dpeaa)DE-He213 Energy prediction (dpeaa)DE-He213 Energy datasets (dpeaa)DE-He213 Inverse modeling (dpeaa)DE-He213 Cetin, Kristen S. aut Enthalten in Current sustainable renewable energy reports Cham : Springer Internat. Publ., 2014 5(2018), 1 vom: 25. Jan., Seite 76-85 (DE-627)780378741 (DE-600)2760353-2 2196-3010 nnns volume:5 year:2018 number:1 day:25 month:01 pages:76-85 https://dx.doi.org/10.1007/s40518-018-0099-3 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2018 1 25 01 76-85
allfieldsGer	10.1007/s40518-018-0099-3 doi (DE-627)SPR03656852X (SPR)s40518-018-0099-3-e DE-627 ger DE-627 rakwb eng Do, Huyen verfasserin aut Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. Summary The literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings. Energy consumption (dpeaa)DE-He213 Residential buildings (dpeaa)DE-He213 Energy prediction (dpeaa)DE-He213 Energy datasets (dpeaa)DE-He213 Inverse modeling (dpeaa)DE-He213 Cetin, Kristen S. aut Enthalten in Current sustainable renewable energy reports Cham : Springer Internat. Publ., 2014 5(2018), 1 vom: 25. Jan., Seite 76-85 (DE-627)780378741 (DE-600)2760353-2 2196-3010 nnns volume:5 year:2018 number:1 day:25 month:01 pages:76-85 https://dx.doi.org/10.1007/s40518-018-0099-3 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2018 1 25 01 76-85
allfieldsSound	10.1007/s40518-018-0099-3 doi (DE-627)SPR03656852X (SPR)s40518-018-0099-3-e DE-627 ger DE-627 rakwb eng Do, Huyen verfasserin aut Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. Summary The literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings. Energy consumption (dpeaa)DE-He213 Residential buildings (dpeaa)DE-He213 Energy prediction (dpeaa)DE-He213 Energy datasets (dpeaa)DE-He213 Inverse modeling (dpeaa)DE-He213 Cetin, Kristen S. aut Enthalten in Current sustainable renewable energy reports Cham : Springer Internat. Publ., 2014 5(2018), 1 vom: 25. Jan., Seite 76-85 (DE-627)780378741 (DE-600)2760353-2 2196-3010 nnns volume:5 year:2018 number:1 day:25 month:01 pages:76-85 https://dx.doi.org/10.1007/s40518-018-0099-3 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2018 1 25 01 76-85
language	English
source	Enthalten in Current sustainable renewable energy reports 5(2018), 1 vom: 25. Jan., Seite 76-85 volume:5 year:2018 number:1 day:25 month:01 pages:76-85
sourceStr	Enthalten in Current sustainable renewable energy reports 5(2018), 1 vom: 25. Jan., Seite 76-85 volume:5 year:2018 number:1 day:25 month:01 pages:76-85
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Energy consumption Residential buildings Energy prediction Energy datasets Inverse modeling
isfreeaccess_bool	false
container_title	Current sustainable renewable energy reports
authorswithroles_txt_mv	Do, Huyen @@aut@@ Cetin, Kristen S. @@aut@@
publishDateDaySort_date	2018-01-25T00:00:00Z
hierarchy_top_id	780378741
id	SPR03656852X
language_de	englisch
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spellingShingle	Do, Huyen misc Energy consumption misc Residential buildings misc Energy prediction misc Energy datasets misc Inverse modeling Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods
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topic_title	Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods Energy consumption (dpeaa)DE-He213 Residential buildings (dpeaa)DE-He213 Energy prediction (dpeaa)DE-He213 Energy datasets (dpeaa)DE-He213 Inverse modeling (dpeaa)DE-He213
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title	Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods
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title_full	Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods
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author_browse	Do, Huyen Cetin, Kristen S.
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title_sort	residential building energy consumption: a review of energy data availability, characteristics, and energy performance prediction methods
title_auth	Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods
abstract	Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. Summary The literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings. © Springer International Publishing AG, part of Springer Nature 2018
abstractGer	Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. Summary The literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings. © Springer International Publishing AG, part of Springer Nature 2018
abstract_unstemmed	Purpose of Review Residential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. Summary The literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings. © Springer International Publishing AG, part of Springer Nature 2018
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title_short	Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods
url	https://dx.doi.org/10.1007/s40518-018-0099-3
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up_date	2024-07-03T18:24:35.605Z
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This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption. Recent Findings While there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies. 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