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Record W4232315519 · doi:10.22215/etd/2014-10313

Sensitivity Analysis of Two Solar Combisystems Using Newly Developed Hot Water Draw Profiles

2014· dissertation· en· W4232315519 on OpenAlexaboutno aff
Skai Edwards

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSensitivity (control systems)Solar energyEnvironmental scienceWater heatingMagnitude (astronomy)Solar water heatingEnergy performanceEnvironmental engineeringEnergy (signal processing)EngineeringWaste managementMathematicsStatisticsPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

This research addresses the viability of two solar combisystems, which use solar energy to provide both space heating and domestic hot water, for several single family homes in Ontario.Hot water use data from seventy three homes were refined to create twelve different draw profiles representing a variety of consumers.These profiles were divided up based on the magnitude of daily draw and the preferred time of use.It was determined that time of use had little to no effect on performance.However, the magnitude of a daily draw was seen to have a significant effect on the final energy use of a home.Two different combisystem plant configurations, a single tank system and a two tank system, were simulated.It was determined that the single tank system offered superior performance.Unless systems were sized to the upper limits tested here, a solar fraction of 50% could not be achieved.iii I would like to first thank my supervisor, Dr. Ian Beausoleil-Morrison.Your knowledge and advice could not have been more useful.My appreciation to my friends and colleagues: GJ, PP, AW, SB, JK, SM, BPK, and SH.You were always there for

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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 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

Citations2
Published2014
Admission routes1
Has abstractyes

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