Simulated building energy demand biases resulting from the use of representative weather stations.
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| Title: | Simulated building energy demand biases resulting from the use of representative weather stations. |
|---|---|
| Authors: | Burleyson, Casey D.1 casey.burleyson@pnnl.gov, Voisin, Nathalie1, Taylor, Z. Todd1, Xie, Yulong1, Kraucunas, Ian1 |
| Source: | Applied Energy. Jan2018, Vol. 209, p516-528. 13p. |
| Subjects: | Computer simulation of weather forecasting, Building information modeling, Weather forecasting, Climate research, Energy consumption & climate |
| Geographic Terms: | United States |
| Abstract: | Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in temperature and load biases from using an increasing number of weather stations over the western U.S. Our novel approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to evaluate the ability to capture weather patterns across different seasons. Using 8 stations across the western U.S., one from each IECC climate zone, results in an average absolute summertime temperature bias of ∼4.0 °C with respect to a high-resolution gridded dataset. The mean absolute bias drops to ∼1.5 °C using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.5%. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20–40% bias of peak building loads during both summer and winter, a significant error for capacity expansion planners who may use these types of simulations. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 126514496 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Simulated building energy demand biases resulting from the use of representative weather stations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Burleyson%2C+Casey+D%2E%22">Burleyson, Casey D.</searchLink><relatesTo>1</relatesTo><i> casey.burleyson@pnnl.gov</i><br /><searchLink fieldCode="AR" term="%22Voisin%2C+Nathalie%22">Voisin, Nathalie</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Taylor%2C+Z%2E+Todd%22">Taylor, Z. Todd</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xie%2C+Yulong%22">Xie, Yulong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kraucunas%2C+Ian%22">Kraucunas, Ian</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Energy%22">Applied Energy</searchLink>. Jan2018, Vol. 209, p516-528. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+simulation+of+weather+forecasting%22">Computer simulation of weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Building+information+modeling%22">Building information modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+research%22">Climate research</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption+%26+climate%22">Energy consumption & climate</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in temperature and load biases from using an increasing number of weather stations over the western U.S. Our novel approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to evaluate the ability to capture weather patterns across different seasons. Using 8 stations across the western U.S., one from each IECC climate zone, results in an average absolute summertime temperature bias of ∼4.0 °C with respect to a high-resolution gridded dataset. The mean absolute bias drops to ∼1.5 °C using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.5%. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20–40% bias of peak building loads during both summer and winter, a significant error for capacity expansion planners who may use these types of simulations. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.apenergy.2017.08.244 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 516 Subjects: – SubjectFull: Computer simulation of weather forecasting Type: general – SubjectFull: Building information modeling Type: general – SubjectFull: Weather forecasting Type: general – SubjectFull: Climate research Type: general – SubjectFull: Energy consumption & climate Type: general – SubjectFull: United States Type: general Titles: – TitleFull: Simulated building energy demand biases resulting from the use of representative weather stations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Burleyson, Casey D. – PersonEntity: Name: NameFull: Voisin, Nathalie – PersonEntity: Name: NameFull: Taylor, Z. Todd – PersonEntity: Name: NameFull: Xie, Yulong – PersonEntity: Name: NameFull: Kraucunas, Ian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 03062619 Numbering: – Type: volume Value: 209 Titles: – TitleFull: Applied Energy Type: main |
| ResultId | 1 |
