Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes
To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yilmaz, S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan - Hering, Alexandra ELSEVIER, 2019, the international journal of the political, economic, planning, environmental and social aspects of energy, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:133 ; year:2019 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.enpol.2019.110909 |
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ELV048241393 |
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| 520 | |a To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). | ||
| 520 | |a To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). | ||
| 650 | 7 | |a Cluster analysis |2 Elsevier | |
| 650 | 7 | |a Multinomial regression |2 Elsevier | |
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| 650 | 7 | |a Demand response |2 Elsevier | |
| 650 | 7 | |a Hurdle model |2 Elsevier | |
| 650 | 7 | |a Household electricity load profiles |2 Elsevier | |
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10.1016/j.enpol.2019.110909 doi GBV00000000000785.pica (DE-627)ELV048241393 (ELSEVIER)S0301-4215(19)30487-2 DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Yilmaz, S. verfasserin aut Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). Cluster analysis Elsevier Multinomial regression Elsevier Load shifting Elsevier Demand response Elsevier Hurdle model Elsevier Household electricity load profiles Elsevier Weber, S. oth Patel, M.K. oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:133 year:2019 pages:0 https://doi.org/10.1016/j.enpol.2019.110909 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 133 2019 0 |
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10.1016/j.enpol.2019.110909 doi GBV00000000000785.pica (DE-627)ELV048241393 (ELSEVIER)S0301-4215(19)30487-2 DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Yilmaz, S. verfasserin aut Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). Cluster analysis Elsevier Multinomial regression Elsevier Load shifting Elsevier Demand response Elsevier Hurdle model Elsevier Household electricity load profiles Elsevier Weber, S. oth Patel, M.K. oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:133 year:2019 pages:0 https://doi.org/10.1016/j.enpol.2019.110909 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 133 2019 0 |
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10.1016/j.enpol.2019.110909 doi GBV00000000000785.pica (DE-627)ELV048241393 (ELSEVIER)S0301-4215(19)30487-2 DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Yilmaz, S. verfasserin aut Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). Cluster analysis Elsevier Multinomial regression Elsevier Load shifting Elsevier Demand response Elsevier Hurdle model Elsevier Household electricity load profiles Elsevier Weber, S. oth Patel, M.K. oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:133 year:2019 pages:0 https://doi.org/10.1016/j.enpol.2019.110909 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 133 2019 0 |
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10.1016/j.enpol.2019.110909 doi GBV00000000000785.pica (DE-627)ELV048241393 (ELSEVIER)S0301-4215(19)30487-2 DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Yilmaz, S. verfasserin aut Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). Cluster analysis Elsevier Multinomial regression Elsevier Load shifting Elsevier Demand response Elsevier Hurdle model Elsevier Household electricity load profiles Elsevier Weber, S. oth Patel, M.K. oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:133 year:2019 pages:0 https://doi.org/10.1016/j.enpol.2019.110909 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 133 2019 0 |
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10.1016/j.enpol.2019.110909 doi GBV00000000000785.pica (DE-627)ELV048241393 (ELSEVIER)S0301-4215(19)30487-2 DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Yilmaz, S. verfasserin aut Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). Cluster analysis Elsevier Multinomial regression Elsevier Load shifting Elsevier Demand response Elsevier Hurdle model Elsevier Household electricity load profiles Elsevier Weber, S. oth Patel, M.K. oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:133 year:2019 pages:0 https://doi.org/10.1016/j.enpol.2019.110909 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 133 2019 0 |
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Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes |
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To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). |
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To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). |
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To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm). |
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