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Record W7082966807 · doi:10.1016/j.csite.2025.107105

Harnessing fuel cell and PVT for cogeneration in buildings: A comparative analysis in different climates

2025· article· en· W7082966807 on OpenAlexaboutno aff

Bibliographic record

VenueCase Studies in Thermal Engineering · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsnot available
FundersAl-Zaytoonah University of Jordan
KeywordsCogenerationBoiler (water heating)Primary energyTemperate climateSolid oxide fuel cellEnergy systemGreenhouse gasEfficient energy useElectricity generation

Abstract

fetched live from OpenAlex

This study compares the performance of two hybrid residential cogeneration systems—a photovoltaic–thermal (PVT) system and a solid oxide fuel cell (SOFC) system. Both systems were integrated with a gas boiler under three distinct climatic conditions: Mediterranean (Amman), semi-continental (Toronto), and temperate seasonal (Tokyo). A two-story single-family home with a 4,500 kWh annual energy demand was simulated in Polysun to evaluate primary energy use, CO 2 emissions, and economic feasibility. The SOFC + boiler system achieved the highest energy efficiency and CO 2 reductions across all climates, with primary energy factors exceeding 4.0 in Amman and Tokyo, and 2.57 in Toronto, alongside annual CO 2 savings above 2,500 kg. While less effective environmentally, the PVT + boiler system showed better economic performance, reaching net present values up to $30,765 and generation costs as low as $0.23/kWh. Climate strongly influenced system outcomes: colder climates favored SOFC performance due to higher thermal efficiency, while sunnier climates enhanced PVT output. These findings close a research gap, offering climate-based insights to advance global low-carbon residential energy strategies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.781

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 head0.037
GPT teacher head0.345
Teacher spread0.307 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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