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Record W6983160844

Life Cycle Optimization of a Residential Solar Combisystem for Minimum Cost, Energy Use and Exergy Destroyed

2013· dissertation· en· W6983160844 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2013
Typedissertation
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionWork (physics)Filter (signal processing)CentenarianArticular cartilage damageGloom
DOInot available

Abstract

fetched live from OpenAlex

This thesis presents the optimization of a model of a solar combisystem installed in an energy efficient house in the climate of Montreal, Quebec. The work presented in the thesis includes: 1) A methodology for the optimization of a solar collector system based on four different objective functions; 2) The development of a computer-based platform for combisystem optimization; 3) Recommendations for the optimal configurations of a solar combisystem to minimize life cycle cost, life cycle energy use and life cycle exergy destroyed; and 4) The analysis of the performance of the hybrid stochastic, evolutionary and deterministic optimization approach.
\nThe optimizations, using is a hybrid particle swarm and Hooke-Jeeves optimization algorithm, were able to reduce the life cycle cost of the combisystem by 19%, the life cycle energy use by 24%, the life cycle exergy destroyed by 33% and 24% for the technical boundary and physical boundary, respectively.
\nDue to the high cost of the solar collector technologies and the low price of electricity in Quebec, none of the configurations have acceptable financial payback periods. However, all of the configurations have energy payback times within 7 years.
\nFor the life cycle exergy destroyed, using the technical boundary favors the use of electricity over solar energy due to the low exergy efficiency of the solar collectors. Using the physical boundary, on the other hand, favors the use of solar energy over electricity.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.029
GPT teacher head0.255
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 teacher head, not a consensus.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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