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Record W4322208333 · doi:10.1016/j.cles.2023.100060

Detailed profiling of high-rise building energy consumption in extremely hot and humid climate

2023· article· en· W4322208333 on OpenAlexaff
Athar Kamal, Sambhaji T. Kadam, Danlin Hou, Ibrahim Hassan, Liangzhu Wang, Nurettin Sezer, Mohammad Azizur Rahman

Bibliographic record

VenueCleaner Energy Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsConcordia University
FundersQatar National Research Fund
KeywordsApartmentEnvironmental scienceEnergy consumptionCooling loadClimate zonesOccupancyConsumption (sociology)Architectural engineeringCivil engineeringEngineeringGeographyAir conditioningPhysical geographyMechanical engineering

Abstract

fetched live from OpenAlex

The extremely hot and humid nature of the Gulf Cooperation Council necessitates cooling. There is a dearth of literature that addresses the energy consumption profile of high-rise buildings in this climate, which is important for the fair distribution of costs among end users according to their usage. This study aims to address the literature gap by studying the cooling requirements of a representative tall residential building in an extremely hot and humid climate. Sensitivity analysis of 14 building characteristics against annual cooling load reveals that Window-to-Wall-Ratio (WWR) and orientation parameters sway anywhere between the most significant and the least significant attributes, respectively. From the analysis, a significant discrepancy in cooling consumption has been observed between the first and top floors, with an average of around 140% more. Furthermore, sensitivity analysis revealed that equipment power density is a dominating factor at the apartment level, while the floor number dominates in the whole building. A parametric analysis indicates that building rotation can increase or decrease energy consumption up to 4.0 kWh/m2/month or 5.6 kWh/m2/month, depending on the apartment's location. This study would facilitate especially the planning stage of the buildings in a whole district and operation-related decisions of the corresponding district cooling plant in the hot and humid 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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.808

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 head0.015
GPT teacher head0.212
Teacher spread0.196 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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