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Record W3087037734 · doi:10.3390/su12187772

Hybrid CHP/Geothermal Borehole System for Multi-Family Building in Heating Dominated Climates

2020· article· en· W3087037734 on OpenAlexaboutno aff
Saeed Alqaed, Jawed Mustafa, Kevin P. Hallinan, Rodwan Elhashmi

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
FundersUniversity of Dayton
KeywordsBoreholeHeat pumpEnvironmental scienceThermal energy storageGeothermal gradientHeating systemElectric powerGeothermal heatingEngineeringGeothermal energyMechanical engineeringPower (physics)GeologyHeat exchanger

Abstract

fetched live from OpenAlex

A conventional ground-coupled heat pump (GCHP) can be used to supplement heat rejection or extraction, creating a hybrid system that is cost-effective for certainly unbalanced climes. This research explores the possibility for a hybrid GCHP to use excess heat from a combined heat power (CHP) unit of natural gas in a heating-dominated environment for smart cities. A design for a multi-family residential building is considered, with a CHP sized to meet the average electrical load of the building. The constant electric output of the CHP is used directly, stored for later use in a battery, or sold back to the grid. Part of the thermal output provides the building with hot water, and the rest is channeled into the GCHP borehole array to support the building’s large heating needs. Consumption and weather data are used to predict hourly loads over a year for a specific multi-family residence. Simulations of the energies exchanged between system components are performed, and a cost model is minimized over CHP size, battery storage capacity, number of boreholes, and depth of the borehole. Results indicate a greater cost advantage for the design in a severely heated (Canada) climate than in a moderately imbalanced (Ohio) climate.

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)0.001
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 score0.388
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.276
Teacher spread0.256 · 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

Citations31
Published2020
Admission routes1
Has abstractyes

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