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Record W4244250304 · doi:10.32920/ryerson.14663082

Estimating power consumption in City of Toronto: a case study

2021· preprint· en· W4244250304 on OpenAlexaffabout
Syed Muhammad Shees Saeed

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsElectricityElectricity demandElectricity generationRenewable energyConsumption (sociology)Environmental economicsPeak demandElectricity retailingEnergy demandBusinessEconomicsPower (physics)Electricity marketEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Ontario’s energy system provides reliable and clean energy to the province. The demand of electricity is rising throughout the world, thus Ontario’s goal is to maintain the demand and generation of electricity. In this report we have discussed the electricity demand of Ontario and divided the sectors into categorical data of electricity and studied peak hour demands of Toronto. First, we have briefly discussed the introduction, which includes the history, geographical location and socio-economic importance of Toronto. Then in the literature review we have highlighted Ontario’s generation of electricity, which is produced by various renewable energy sources and have further discussed their drawbacks. The survey is focused on the demand of electricity in Toronto by calculating the requirement and then distributing the data into 24 hours, from which we have studied peak hours demand in various categories such as residential buildings, offices, shops etc. The purpose of this survey is to monitor the electricity demand in order to reduce power outages and blackouts due to technical issues.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.991

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)0.0010.000

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 head0.030
GPT teacher head0.282
Teacher spread0.252 · 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 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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