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Record W2145163959 · doi:10.1080/17512549.2014.890541

Heating energy penalties of cool roofs: the effect of snow accumulation on roofs

2014· article· en· W2145163959 on OpenAlexafffundabout
Mirata Hosseini, Hashem Akbari

Bibliographic record

VenueAdvances in Building Energy Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsRoofReflective surfacesSnowEnvironmental scienceMeteorologyAtmospheric sciencesMorningAir conditioningEngineeringCivil engineeringGeographyGeology

Abstract

fetched live from OpenAlex

Utilizing a cool roof is an efficient way to reduce the cooling energy use of a building. Cool roofs, however, may increase heating energy use in winter. In cold climates, during winter the sun angle is low, days are short, the sky is cloudy, and most the heating occurs early in the morning or in the evening hours when the solar intensity is low. In addition, the roof may be covered with snow for most of the heating season. All these lead to a lower (than what is commonly thought) winter time heating penalties for cool roofs. We used DOE-2.1E to simulate energy consumption in an office building in four cold climate cities in North America: Anchorage (AK), Milwaukee (WI), Montreal (QC), and Toronto (ON). The effect of the sun angle, clouds, daytime duration, and heating schedules can be modelled with existing capabilities of DOE-2. Snow on the roof provides an additional layer of insulation and increases the solar reflectance of the roof. To simulate the effect of snow, we defined a DOE-2 function consisting of U-value and absorptivity of the roof on a daily basis to simulate four different types of snow on the roof. We used an average of six years meteorological data from National Oceanic and Atmospheric Administration and Environment Canada to estimate the snow thickness on the roof. Results show that the heating penalties of cool roof are significantly lower (than what is commonly thought) considering snow on the roof. The annual heating energy consumption of the building with dark and cool roof without considering the snow are 85 and 88 GJ/100 m2, respectively (3 GJ/100 m2 penalty for cool roof) in Anchorage, whereas, the annual heating energy for the dark and cool roof considering the effect of late-winter packed snow are 83 and 84 GJ/100 m2, respectively (1 GJ/100 m2 penalty for the cool roof). For a typical office building with electricity as cooling fuel and natural gas as heating fuel, cool roofs save 0.08 $ m–2 in Montreal and in Toronto the saving for cool roof is 0.04 $ m–2 (not accounting for the effect of peak demand savings and potential downsizing of the heating, ventilation, and air conditioning systems).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.353
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations42
Published2014
Admission routes3
Has abstractyes

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