Modelling household electricity load profiles based on Danish time-use survey data

The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Foteinaki, Kyriaki [verfasserIn] Li, Rongling [verfasserIn] Rode, Carsten [verfasserIn] Andersen, Rune Korsholm [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Household electricity profile Time-use survey data Load modelling Daily load profiles Residential building

Übergeordnetes Werk:	Enthalten in: Energy and buildings - Amsterdam [u.a.] : Elsevier Science, 1977, 202
Übergeordnetes Werk:	volume:202

DOI / URN:	10.1016/j.enbuild.2019.109355

Katalog-ID:	ELV002983648

Internformat


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520			\|a The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households using Danish time-use survey (DTUS) data. The approach was to combine appliance ownership and power ratings with occupant activities from the DTUS. Two modelling approaches were implemented: in the first approach, the occupant activities profiles from the DTUS were used directly to determine activities at 10-minute intervals. In the second approach, the probabilities of starting time and duration of the occupant activities were used to determine activities. In both approaches, appliance use was assigned to the energy-related activities. The set of appliances used in each activity was determined from a national database of appliance ownership, and the appliances’ power was calibrated using information from apartments in Copenhagen, Denmark. The modelled daily electricity load profile was compared with three measured datasets of varying sizes and from different parts of Denmark. Both approaches captured important qualitative characteristics of the measured load profiles. However, the first approach used a more simple method and resulted in smaller errors than the second approach.
650		4	\|a Household electricity profile
650		4	\|a Time-use survey data
650		4	\|a Load modelling
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matchkey_str	foteinakikyriakilironglingrodecarstenand:2019----:oelnhueodlcrctlapoiebsdna
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publishDate	2019
allfields	10.1016/j.enbuild.2019.109355 doi (DE-627)ELV002983648 (ELSEVIER)S0378-7788(18)33866-0 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Foteinaki, Kyriaki verfasserin (orcid)0000-0002-1341-6633 aut Modelling household electricity load profiles based on Danish time-use survey data 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households using Danish time-use survey (DTUS) data. The approach was to combine appliance ownership and power ratings with occupant activities from the DTUS. Two modelling approaches were implemented: in the first approach, the occupant activities profiles from the DTUS were used directly to determine activities at 10-minute intervals. In the second approach, the probabilities of starting time and duration of the occupant activities were used to determine activities. In both approaches, appliance use was assigned to the energy-related activities. The set of appliances used in each activity was determined from a national database of appliance ownership, and the appliances’ power was calibrated using information from apartments in Copenhagen, Denmark. The modelled daily electricity load profile was compared with three measured datasets of varying sizes and from different parts of Denmark. Both approaches captured important qualitative characteristics of the measured load profiles. However, the first approach used a more simple method and resulted in smaller errors than the second approach. Household electricity profile Time-use survey data Load modelling Daily load profiles Residential building Li, Rongling verfasserin aut Rode, Carsten verfasserin (orcid)0000-0001-7485-3119 aut Andersen, Rune Korsholm verfasserin (orcid)0000-0003-0080-1580 aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 202 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:202 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 202
spelling	10.1016/j.enbuild.2019.109355 doi (DE-627)ELV002983648 (ELSEVIER)S0378-7788(18)33866-0 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Foteinaki, Kyriaki verfasserin (orcid)0000-0002-1341-6633 aut Modelling household electricity load profiles based on Danish time-use survey data 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households using Danish time-use survey (DTUS) data. The approach was to combine appliance ownership and power ratings with occupant activities from the DTUS. Two modelling approaches were implemented: in the first approach, the occupant activities profiles from the DTUS were used directly to determine activities at 10-minute intervals. In the second approach, the probabilities of starting time and duration of the occupant activities were used to determine activities. In both approaches, appliance use was assigned to the energy-related activities. The set of appliances used in each activity was determined from a national database of appliance ownership, and the appliances’ power was calibrated using information from apartments in Copenhagen, Denmark. The modelled daily electricity load profile was compared with three measured datasets of varying sizes and from different parts of Denmark. Both approaches captured important qualitative characteristics of the measured load profiles. However, the first approach used a more simple method and resulted in smaller errors than the second approach. Household electricity profile Time-use survey data Load modelling Daily load profiles Residential building Li, Rongling verfasserin aut Rode, Carsten verfasserin (orcid)0000-0001-7485-3119 aut Andersen, Rune Korsholm verfasserin (orcid)0000-0003-0080-1580 aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 202 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:202 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 202
allfields_unstemmed	10.1016/j.enbuild.2019.109355 doi (DE-627)ELV002983648 (ELSEVIER)S0378-7788(18)33866-0 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Foteinaki, Kyriaki verfasserin (orcid)0000-0002-1341-6633 aut Modelling household electricity load profiles based on Danish time-use survey data 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households using Danish time-use survey (DTUS) data. The approach was to combine appliance ownership and power ratings with occupant activities from the DTUS. Two modelling approaches were implemented: in the first approach, the occupant activities profiles from the DTUS were used directly to determine activities at 10-minute intervals. In the second approach, the probabilities of starting time and duration of the occupant activities were used to determine activities. In both approaches, appliance use was assigned to the energy-related activities. The set of appliances used in each activity was determined from a national database of appliance ownership, and the appliances’ power was calibrated using information from apartments in Copenhagen, Denmark. The modelled daily electricity load profile was compared with three measured datasets of varying sizes and from different parts of Denmark. Both approaches captured important qualitative characteristics of the measured load profiles. However, the first approach used a more simple method and resulted in smaller errors than the second approach. Household electricity profile Time-use survey data Load modelling Daily load profiles Residential building Li, Rongling verfasserin aut Rode, Carsten verfasserin (orcid)0000-0001-7485-3119 aut Andersen, Rune Korsholm verfasserin (orcid)0000-0003-0080-1580 aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 202 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:202 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 202
allfieldsGer	10.1016/j.enbuild.2019.109355 doi (DE-627)ELV002983648 (ELSEVIER)S0378-7788(18)33866-0 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Foteinaki, Kyriaki verfasserin (orcid)0000-0002-1341-6633 aut Modelling household electricity load profiles based on Danish time-use survey data 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households using Danish time-use survey (DTUS) data. The approach was to combine appliance ownership and power ratings with occupant activities from the DTUS. Two modelling approaches were implemented: in the first approach, the occupant activities profiles from the DTUS were used directly to determine activities at 10-minute intervals. In the second approach, the probabilities of starting time and duration of the occupant activities were used to determine activities. In both approaches, appliance use was assigned to the energy-related activities. The set of appliances used in each activity was determined from a national database of appliance ownership, and the appliances’ power was calibrated using information from apartments in Copenhagen, Denmark. The modelled daily electricity load profile was compared with three measured datasets of varying sizes and from different parts of Denmark. Both approaches captured important qualitative characteristics of the measured load profiles. However, the first approach used a more simple method and resulted in smaller errors than the second approach. Household electricity profile Time-use survey data Load modelling Daily load profiles Residential building Li, Rongling verfasserin aut Rode, Carsten verfasserin (orcid)0000-0001-7485-3119 aut Andersen, Rune Korsholm verfasserin (orcid)0000-0003-0080-1580 aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 202 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:202 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 202
allfieldsSound	10.1016/j.enbuild.2019.109355 doi (DE-627)ELV002983648 (ELSEVIER)S0378-7788(18)33866-0 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Foteinaki, Kyriaki verfasserin (orcid)0000-0002-1341-6633 aut Modelling household electricity load profiles based on Danish time-use survey data 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households using Danish time-use survey (DTUS) data. The approach was to combine appliance ownership and power ratings with occupant activities from the DTUS. Two modelling approaches were implemented: in the first approach, the occupant activities profiles from the DTUS were used directly to determine activities at 10-minute intervals. In the second approach, the probabilities of starting time and duration of the occupant activities were used to determine activities. In both approaches, appliance use was assigned to the energy-related activities. The set of appliances used in each activity was determined from a national database of appliance ownership, and the appliances’ power was calibrated using information from apartments in Copenhagen, Denmark. The modelled daily electricity load profile was compared with three measured datasets of varying sizes and from different parts of Denmark. Both approaches captured important qualitative characteristics of the measured load profiles. However, the first approach used a more simple method and resulted in smaller errors than the second approach. Household electricity profile Time-use survey data Load modelling Daily load profiles Residential building Li, Rongling verfasserin aut Rode, Carsten verfasserin (orcid)0000-0001-7485-3119 aut Andersen, Rune Korsholm verfasserin (orcid)0000-0003-0080-1580 aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 202 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:202 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 202
language	English
source	Enthalten in Energy and buildings 202 volume:202
sourceStr	Enthalten in Energy and buildings 202 volume:202
format_phy_str_mv	Article
bklname	Heizungstechnik Lüftungstechnik Klimatechnik Technischer Ausbau Bauphysik Bautenschutz Bauökologie Baubiologie
institution	findex.gbv.de
topic_facet	Household electricity profile Time-use survey data Load modelling Daily load profiles Residential building
dewey-raw	690
isfreeaccess_bool	false
container_title	Energy and buildings
authorswithroles_txt_mv	Foteinaki, Kyriaki @@aut@@ Li, Rongling @@aut@@ Rode, Carsten @@aut@@ Andersen, Rune Korsholm @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	308448030
dewey-sort	3690
id	ELV002983648
language_de	englisch
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author	Foteinaki, Kyriaki
spellingShingle	Foteinaki, Kyriaki ddc 690 bkl 52.42 bkl 56.50 bkl 56.55 bkl 56.65 misc Household electricity profile misc Time-use survey data misc Load modelling misc Daily load profiles misc Residential building Modelling household electricity load profiles based on Danish time-use survey data
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topic_title	690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Modelling household electricity load profiles based on Danish time-use survey data Household electricity profile Time-use survey data Load modelling Daily load profiles Residential building
topic	ddc 690 bkl 52.42 bkl 56.50 bkl 56.55 bkl 56.65 misc Household electricity profile misc Time-use survey data misc Load modelling misc Daily load profiles misc Residential building
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title	Modelling household electricity load profiles based on Danish time-use survey data
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title_full	Modelling household electricity load profiles based on Danish time-use survey data
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title_sort	modelling household electricity load profiles based on danish time-use survey data
title_auth	Modelling household electricity load profiles based on Danish time-use survey data
abstract	The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households using Danish time-use survey (DTUS) data. The approach was to combine appliance ownership and power ratings with occupant activities from the DTUS. Two modelling approaches were implemented: in the first approach, the occupant activities profiles from the DTUS were used directly to determine activities at 10-minute intervals. In the second approach, the probabilities of starting time and duration of the occupant activities were used to determine activities. In both approaches, appliance use was assigned to the energy-related activities. The set of appliances used in each activity was determined from a national database of appliance ownership, and the appliances’ power was calibrated using information from apartments in Copenhagen, Denmark. The modelled daily electricity load profile was compared with three measured datasets of varying sizes and from different parts of Denmark. Both approaches captured important qualitative characteristics of the measured load profiles. However, the first approach used a more simple method and resulted in smaller errors than the second approach.
abstractGer	The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households using Danish time-use survey (DTUS) data. The approach was to combine appliance ownership and power ratings with occupant activities from the DTUS. Two modelling approaches were implemented: in the first approach, the occupant activities profiles from the DTUS were used directly to determine activities at 10-minute intervals. In the second approach, the probabilities of starting time and duration of the occupant activities were used to determine activities. In both approaches, appliance use was assigned to the energy-related activities. The set of appliances used in each activity was determined from a national database of appliance ownership, and the appliances’ power was calibrated using information from apartments in Copenhagen, Denmark. The modelled daily electricity load profile was compared with three measured datasets of varying sizes and from different parts of Denmark. Both approaches captured important qualitative characteristics of the measured load profiles. However, the first approach used a more simple method and resulted in smaller errors than the second approach.
abstract_unstemmed	The relationship among occupants’ presence, activities and appliance use is essential for households’ energy use. In the present work, we aimed to link occupants’ energy-related activities to electricity demand, in order to obtain a representative daily electricity load profile for Danish households using Danish time-use survey (DTUS) data. The approach was to combine appliance ownership and power ratings with occupant activities from the DTUS. Two modelling approaches were implemented: in the first approach, the occupant activities profiles from the DTUS were used directly to determine activities at 10-minute intervals. In the second approach, the probabilities of starting time and duration of the occupant activities were used to determine activities. In both approaches, appliance use was assigned to the energy-related activities. The set of appliances used in each activity was determined from a national database of appliance ownership, and the appliances’ power was calibrated using information from apartments in Copenhagen, Denmark. The modelled daily electricity load profile was compared with three measured datasets of varying sizes and from different parts of Denmark. Both approaches captured important qualitative characteristics of the measured load profiles. However, the first approach used a more simple method and resulted in smaller errors than the second approach.
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title_short	Modelling household electricity load profiles based on Danish time-use survey data
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Modelling household electricity load profiles based on Danish time-use survey data

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