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Adaptive Models for the Energy Efficiency of Building Envelopes

2018· article· en· W2792387534 on OpenAlexvenueno aff
Martino Milardi

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Planning and Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

The debate on energy efficiency in buildings has already established the strategic role of the envelope to achieve the control objectives of consumption and housing quality. The study shows, it ranks in the innovative experimentation scenario, following the directions of the regulations 2010/31/EU (EPBD 2) and its objective is the realization of a building envelope "adaptive", characterized by the ability to dynamically respond to stress from the environmental context with which it relates. The experiment aimed at perfecting a model of intervention that can guide the design choices towards elements that make up the building envelope, characterized by a strong interactive-adaptive component. This is based on the combination of: layers of innovative envelope, systems using RES and SMART management requirements of the system, with a focus on intelligent control energy flows between external/internal and adaptive performance of the layers. The mode of "deferred layering" in relation to the orientation of the building and to climate periods, while for the control of wrap responses subjected to environmental stress, the research adopts BEMS & BIM systems and other related criteria will be defined. The experiment aims to satisfy the requirements to improve the energy performance of buildings by reducing the impacts (emissions), through the development of a technical system. The results obtained from inspections in progress, show that the constituent layers that envelope react in a synergistic way and adapted to different climatic conditions, ensuring high-quality performance, in line with the energy efficiency targets established by regulatory standards.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2018
Admission routes1
Has abstractyes

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