Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Identifying rural high energy intensity residential buildings using metered data
https://orcid.org/0000-0003-2242-3082
Highlights: Hierarchical time-series clustering is used to characterize rural energy users. Size-based clustering framework corrects the inherent bias of traditional metrics. 5% and 7% of users are classified as high intensity users for electricity and natural gas. High intensity electricity users are on average 44% more energy intensive. High intensity natural gas users are on average 30% more energy intensive.
Abstract The residential housing stock in rural areas of the U.S. is often older and less energy-efficient than homes built in urban areas. To characterize the energy performance of these buildings and facilitate their energy efficiency upgrades, this study provides a framework for analyzing building energy time-series data to identify buildings with similar temporal energy performance patterns. Using an unsupervised hierarchical time-series clustering algorithm, this framework aims to identify highly inefficient buildings as strong candidates for energy efficiency improvement programs. Weather-normalized electricity and natural gas consumption data from 320 properties in the rural town of Bloomfield, Iowa, covering a six-year reporting period from 2014 to 2019 is used to optimally cluster energy consumption data into two groups, including average and high intensity energy users. To correct the underlying bias of the commonly used area-normalized energy use intensity (EUI) indexes towards smaller homes, the proposed clustering algorithm only compares the EUI of homes with similar floor areas. User types are defined for electricity and natural gas consumption separately to allow for tailored actions and policy recommendations. Accordingly, 5% and 7% of residences in the dataset were classified as high intensity users for electricity and natural gas, respectively. Of the remaining, 88% of homes were classified as average intensity users for both energy types. Only 2 properties in the dataset (less than 1%) were identified to be high intensity users for both electricity and natural gas. Homes classified as high intensity electricity and natural gas users, although smaller in number, were, on average, 44% and 30% more energy intensive, respectively, when compared to similar-sized average intensity consumers. The results also indicated that older homes are generally more likely to be high intensity natural gas consumers while a relatively higher percentage of newer homes were classified as high intensity electricity users.
Identifying rural high energy intensity residential buildings using metered data
https://orcid.org/0000-0003-2242-3082
Highlights: Hierarchical time-series clustering is used to characterize rural energy users. Size-based clustering framework corrects the inherent bias of traditional metrics. 5% and 7% of users are classified as high intensity users for electricity and natural gas. High intensity electricity users are on average 44% more energy intensive. High intensity natural gas users are on average 30% more energy intensive.
Abstract The residential housing stock in rural areas of the U.S. is often older and less energy-efficient than homes built in urban areas. To characterize the energy performance of these buildings and facilitate their energy efficiency upgrades, this study provides a framework for analyzing building energy time-series data to identify buildings with similar temporal energy performance patterns. Using an unsupervised hierarchical time-series clustering algorithm, this framework aims to identify highly inefficient buildings as strong candidates for energy efficiency improvement programs. Weather-normalized electricity and natural gas consumption data from 320 properties in the rural town of Bloomfield, Iowa, covering a six-year reporting period from 2014 to 2019 is used to optimally cluster energy consumption data into two groups, including average and high intensity energy users. To correct the underlying bias of the commonly used area-normalized energy use intensity (EUI) indexes towards smaller homes, the proposed clustering algorithm only compares the EUI of homes with similar floor areas. User types are defined for electricity and natural gas consumption separately to allow for tailored actions and policy recommendations. Accordingly, 5% and 7% of residences in the dataset were classified as high intensity users for electricity and natural gas, respectively. Of the remaining, 88% of homes were classified as average intensity users for both energy types. Only 2 properties in the dataset (less than 1%) were identified to be high intensity users for both electricity and natural gas. Homes classified as high intensity electricity and natural gas users, although smaller in number, were, on average, 44% and 30% more energy intensive, respectively, when compared to similar-sized average intensity consumers. The results also indicated that older homes are generally more likely to be high intensity natural gas consumers while a relatively higher percentage of newer homes were classified as high intensity electricity users.
Identifying rural high energy intensity residential buildings using metered data
https://orcid.org/0000-0003-2242-3082
Highlights: Hierarchical time-series clustering is used to characterize rural energy users. Size-based clustering framework corrects the inherent bias of traditional metrics. 5% and 7% of users are classified as high intensity users for electricity and natural gas. High intensity electricity users are on average 44% more energy intensive. High intensity natural gas users are on average 30% more energy intensive.
Abstract The residential housing stock in rural areas of the U.S. is often older and less energy-efficient than homes built in urban areas. To characterize the energy performance of these buildings and facilitate their energy efficiency upgrades, this study provides a framework for analyzing building energy time-series data to identify buildings with similar temporal energy performance patterns. Using an unsupervised hierarchical time-series clustering algorithm, this framework aims to identify highly inefficient buildings as strong candidates for energy efficiency improvement programs. Weather-normalized electricity and natural gas consumption data from 320 properties in the rural town of Bloomfield, Iowa, covering a six-year reporting period from 2014 to 2019 is used to optimally cluster energy consumption data into two groups, including average and high intensity energy users. To correct the underlying bias of the commonly used area-normalized energy use intensity (EUI) indexes towards smaller homes, the proposed clustering algorithm only compares the EUI of homes with similar floor areas. User types are defined for electricity and natural gas consumption separately to allow for tailored actions and policy recommendations. Accordingly, 5% and 7% of residences in the dataset were classified as high intensity users for electricity and natural gas, respectively. Of the remaining, 88% of homes were classified as average intensity users for both energy types. Only 2 properties in the dataset (less than 1%) were identified to be high intensity users for both electricity and natural gas. Homes classified as high intensity electricity and natural gas users, although smaller in number, were, on average, 44% and 30% more energy intensive, respectively, when compared to similar-sized average intensity consumers. The results also indicated that older homes are generally more likely to be high intensity natural gas consumers while a relatively higher percentage of newer homes were classified as high intensity electricity users.
Identifying rural high energy intensity residential buildings using metered data
Malekpour Koupaei, Diba (Autor:in) / Cetin, Kristen (Autor:in) / Passe, Ulrike (Autor:in) / Kimber, Anne (Autor:in) / Poleacovschi, Cristina (Autor:in)
Energy and Buildings ; 298
28.09.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
End-Use Metered Data for Commercial Buildings
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