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Record W7017278921

Assessing the Impact of Climate Change on Electricity Planning in Ontario, Canada

2021· dissertation· W7017278921 on OpenAlexfundaboutno aff

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsClimate changeElectricityHydroelectricityMains electricityElectricity demandElectricity system
DOInot available

Abstract

fetched live from OpenAlex

Electricity grid adequacy is the ability of the electric system to supply the electricity required at all times, accounting for derates and outages of system components. Ontario’s electricity needs are primarily met by thermal and hydroelectric generators, shown to be impacted by climate conditions. We quantify the effects of climate change on Ontario’s future demand and capacity based on the highest risk climate change scenario (RCP 8.5) for the year 2040. The main climate change variables considered are temperature, humidity, and precipitation. Very minor reductions in summer effective capacity are expected (approximately 0.2%), together with a moderate increase in summer demand (approximately 6%). These results only reflect the impacts of climate change relative to the current climate. Although the Independent Electricity System Operator appears to capture enough variability in their grid adequacy estimates, it may be wise to add additional butter to capture the impact of climate change discovered here.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.320
Teacher spread0.296 · 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.

Study designSimulation or modeling
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

Citations0
Published2021
Admission routes2
Has abstractyes

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