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Record W2593320170 · doi:10.22488/okstate.17.000529

Heat Pump Capacity Effects on Peak Electricity Consumption and Total Length of Self- and Solar-assisted Shallow Ground Heat Exchanger Networks

2017· article· en· W2593320170 on OpenAlexafffund
Parham Eslami Nejad, Massimo Cimmino, Sophie Hosatte-Ducassy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsNatural Resources Canada
FundersFonds de recherche du Québec – Nature et technologiesNatural Resources Canada
KeywordsHeat exchangerElectricityHeat pumpEnvironmental scienceMaterials sciencePhysicsElectrical engineeringEngineeringThermodynamics

Abstract

fetched live from OpenAlex

A new "self-assisted" Ground Source Heat Pump (GSHP) system configuration is proposed to address the relatively high peak electricity demand of undersized GSHP systems equiped with auxiliary electric heater. In this configuration, ground heat exchangers (GHE) have two independent circuits: the first circuit is used to inject the extra heat produced by the heat pump into the ground during off-peak operations, while the second circuit is used to extract heat in the winter and reject heat in the summer for space heating and cooling, respectively. This configuration is compared against a "solar-assisted" configuration and a conventional single U-tube configuration. An analytical model for shallow GHE networks is used to evaluate the effects of the heat pump nominal capacity and the borehole total length on the total electricity consumption and peak electricity demand of the three configurations. Results show that the self-assisted configuration reduces the peak electricity demand by 47%, in a case with a 29% undersized GHE network and a 16% undersized heat pump nominal capacity, while it increases the total energy consumption by 4.1%. Using a solar-assisted configuration for the same sizing parameters reduces the peak electricity demand by only 6.3% and the total energy consumption by 3.8%.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.992

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 head0.020
GPT teacher head0.232
Teacher spread0.212 · 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 designObservational
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

Citations7
Published2017
Admission routes2
Has abstractyes

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