Heating energy penalties of cool roofs: the effect of snow accumulation on roofs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it