Community energy by design: A simulation-based design workflow using measured data clustering to calibrate Urban Building Energy Models (UBEMs) (Record no. 11669)
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| fixed length control field | 02356nab a2200253 4500 |
| 005 - DATE & TIME | |
| control field | 20210517162207.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210517b ||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Rakha, Tarek |
| 245 ## - TITLE STATEMENT | |
| Title | Community energy by design: A simulation-based design workflow using measured data clustering to calibrate Urban Building Energy Models (UBEMs) |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | Sage, |
| Date of publication, distribution, etc | 2019. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pages | Vol 46, Issue 8, 2019 (1517-1533 p.) |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This paper presents a workflow that informs urban design decisions using measured data clustering to calibrate urban building energy models. The method’s goal is to support urban design in terms of form, building systems configurations, as well as influencing user behavior aspects in the built environment through a systemic analysis of measured data to develop reliable future-case design scenario energy models. Detailed data on appliance-level electricity use are employed via data clustering to calibrate a urban building energy model for the Mueller community in Austin, TX, USA. The data were collected by the Pecan Street Institute for a year in 2014 from consumers in Austin and other surrounding cities. First, collected energy data were restructured and cleaned from corrupt and/or missing information. Second, in order to identify common energy use patterns, a model-based clustering algorithm for functional data was applied. Behavioral/usage profiles are determined through clustering and translated into usage schedules and behaviors. As a result, an urban building energy model built in the urban modeling interface (umi) was calibrated, with fully calibrated and semi-calibrated buildings, within a maximum calibration error margin of 14% for daily-scale averages. Finally, an illustration of calibrated-urban building energy model design case scenarios is presented, and implications on community energy potential effects are discussed. |
| 650 ## - Subject | |
| Subject | Urban design, |
| 650 ## - Subject | |
| Subject | urban simulation, |
| 650 ## - Subject | |
| Subject | model calibration, |
| 650 ## - Subject | |
| Subject | measured data, |
| 650 ## - Subject | |
| Subject | data clustering |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Rawad El Kontar |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 11590 |
| Host Itemnumber | 15512 |
| Place, publisher, and date of publication | Sage 2019. |
| Title | Environment and Planning B: Urban Analytics and City Science |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1177/2399808319841909 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Articles |
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