MétaCan
Menu
Back to cohort
Record W3095659898 · doi:10.3390/en13215680

Analysis of Retrofit Solutions of a Ground Source Heat Pump System: An Italian Case Study

2020· article· en· W3095659898 on OpenAlexaff
Angelo Zarrella, Roberto Zecchin, Philippe Pasquier, Diego Guzzon, Enrico Prataviera, Jacopo Vivian, Michele De Carli, Giuseppe Emmi

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsHeat pumpHybrid heatSizingWork (physics)Air source heat pumpsThermalEnvironmental scienceHeat sinkPassive coolingEngineeringMeteorologyComputer scienceMechanical engineeringHeat exchangerChemistryPhysics

Abstract

fetched live from OpenAlex

Ground coupled heat pumps are a notoriously efficient system for heating and cooling buildings. Sometimes the characteristics of the building and the user’s needs are such that the amount of heat extracted from the ground during the winter season can be considerably different from the amount injected in summer. This situation can cause a progressive cooling or heating of the ground with a negative effect on the energy efficiency and correct operation of the system. In these cases, an accurate sizing has to be done. In systems already built, it could be necessary to intervene a posteriori to remedy an excessive ground thermal drift due to the energy unbalance. In this work, such a situation relating to a real office building in Italy is investigated and several solutions are examined, one of which has been then implemented. In particular, a hybrid heat pump using as heat sink both the ground and external air is compared with common solutions through computer simulations using a dedicated numerical model, which has also been compared with monitoring data. As a result, the hybrid heat pump shows better performance and limits the thermal drift of the ground temperature.

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 score0.427
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.256
Teacher spread0.220 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEnergiesSame topicGeothermal Energy Systems and ApplicationsFrench-language works237,207