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_c11669 _d11669 |
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| 003 | OSt | ||
| 005 | 20210517162207.0 | ||
| 007 | cr aa aaaaa | ||
| 008 | 210517b ||||| |||| 00| 0 eng d | ||
| 100 |
_aRakha, Tarek _946059 |
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| 245 | _aCommunity energy by design: A simulation-based design workflow using measured data clustering to calibrate Urban Building Energy Models (UBEMs) | ||
| 260 |
_bSage, _c2019. |
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| 300 | _aVol 46, Issue 8, 2019 (1517-1533 p.) | ||
| 520 | _aThis 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 |
_aUrban design, _946060 |
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| 650 |
_aurban simulation, _946061 |
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| 650 |
_amodel calibration, _946062 |
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| 650 |
_ameasured data, _946063 |
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| 650 |
_a data clustering _946064 |
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| 700 |
_aRawad El Kontar _946065 |
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| 773 | 0 |
_011590 _915512 _dSage 2019. _t Environment and Planning B: Urban Analytics and City Science |
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| 856 | _uhttps://doi.org/10.1177/2399808319841909 | ||
| 942 |
_2ddc _cART |
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