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The energy retrofit of building façades in 22@ innovation district of Barcelona: energy performance and cost-benefit analysis.

2019· article· en· W2982094058 on OpenAlexaff
Mauro Manca, Prochazkova Zuzana, Umberto Berardi, Pich-Aguilera Felipe, Batlle Teresa

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsContext (archaeology)Thermal comfortDirectiveEnergy performanceThrivingArchitectural engineeringZero-energy buildingEfficient energy useCivil engineeringEnvironmental scienceEngineeringComputer scienceMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract Under the current Energy Performance of Buildings Directive, EU countries must set building energy renovation as a goal for future development of the cities. The 22@ district of Barcelona is one of the most thriving innovation districts of Europe with an increasing market for office buildings. In this framework, the present paper evaluates the effectiveness of a series of strategies considered the real case project of the energy retrofit of an existing building in 22@. In particular, the study presents the results of different scenarios of building retrofits, where simulations of dynamic envelopes are performed, with the inclusion of a conventional ventilated façade, Living Green Walls and Phase Change Material (PCM) for thermal energy storage. The different scenarios are compared in terms of energy performance, enhanced comfort and cost-benefit analysis. The benefits of latent thermal energy storage, improved thermal inertia and evapotranspiration of the vegetated elements are also assessed. Eventually this study helps understanding the feasibility of the implementation of the nZEB standard in energy retrofit of buildings in the specific context of Barcelona and Spain.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.184
Teacher spread0.177 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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