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Record W2330310048 · doi:10.1016/j.egypro.2015.11.365

Investigating the Effect of Control Strategy on the Shift of Energy Consumption in a Building Integrated with PCM Wallboard

2015· article· en· W2330310048 on OpenAlexaff
Arash Bastani, Fariborz Haghighat, Celia Jalon Manzano

Bibliographic record

VenueEnergy Procedia · 2015
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsBuilding envelopePhase-change materialThermal energy storageEnergy consumptionLatent heatThermalEnvelope (radar)Thermal massEnvironmental scienceControl (management)Thermal comfortPeak demandPoint (geometry)Energy (signal processing)Automotive engineeringEngineeringPhase changeComputer scienceElectrical engineeringMeteorologyThermodynamicsElectricityAerospace engineeringEngineering physicsMathematicsPhysics

Abstract

fetched live from OpenAlex

While space conditioning load contributes largely to the grid critical peak, shifting it partially or entirely to the off-peak period could have significant economic impact on both energy supply and demand sides. This shifting technique is accomplished by storing energy during off-peak periods in order to be utilized during peak periods. The building envelope integrated with phase change material (PCM) can provide latent heat thermal energy storage (TES) distributed in its entire surface area and inhibit the enhanced thermal mass in light weight buildings. Storing energy through an appropriate control strategy results in a longer shift of thermal load and lower energy demand. This study numerically investigates the effect of different control strategy on the thermal performance of a building with its envelope integrated with PCM. The simulation results showed that the most efficient control strategy is the one with room set-point temperature imposes the full melting and solidification of the PCM wallboard.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.031
GPT teacher head0.257
Teacher spread0.226 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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