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Record W3046357796 · doi:10.11159/icert20.01

Impact of climate change on building heating and cooling energy demand

2020· article· en· W3046357796 on OpenAlexaffvenueabout
Umberto Berardi

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2020
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsClimate changeEnvironmental scienceEnergy (signal processing)Energy demandEnvironmental economicsEconomicsGeology

Abstract

fetched live from OpenAlex

Office buildings are responsible for a substantial portion of the energy demand in the commercial sector. To better understand and address the impacts of climate change on building energy demand and comfort levels, this keynote will present studies done in different regions (cool-humid, cold-humid, and very cold climate zones) of Canada using climate projections for the 2056-2075 period. This work also investigates the effect of extending thermostat setpoint, as a demand response strategy, on reducing energy demand under future climate conditions. The results indicate a decrease in the heating demand and an increase in the cooling load because of the warmer temperatures on average across Canada. However, the magnitude of change varies significantly among the different selected climate zones (5, 6, and 7). In addition, extending the temperature setpoints would reduce the average energy demand by 8.0-19.2% in Quebec City, 1.8-9.0% in Toronto, and 1.8-9.6% in Vancouver. Finally, the greenhouse gas emissions linked to space heating and cooling of the office building model are determined, illustrating the critical need to transition to low-carbon sources of energy, especially in areas with high emission factors.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2020
Admission routes3
Has abstractyes

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