MétaCan
Menu
Back to cohort
Record W4392400609 · doi:10.5829/ijee.2024.15.04.06

Energy Efficiency of Residential Buildings in Cold and Dry Climates using Simulation-based Comparative Analysis: Case Study in Tabriz

2024· article· en· W4392400609 on OpenAlexaff
Nima Amani, Ardalan Sabamehr

Bibliographic record

VenueIranica Journal of Energy and Environment · 2024
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsConcordia University
Fundersnot available
KeywordsEnvironmental scienceDry climateArchitectural engineeringEngineeringClimatologyGeology

Abstract

fetched live from OpenAlex

The purpose of this research is to analyze the energy of a residential building in the city of Tabriz with a cold and dry climate using energy simulation to provide a model to minimize energy consumption. A comparative model of energy consumption analysis in a three-story building unit with dimensions of 181 square meters is presented using 5 layout modes in the wall, floor, ceiling, window and door. The number of 5 designs with different arrangement of rooms and different number of windows were compared in terms of energy conservation in 51 different diagrams and the optimal energy saving design is selected. In the next step, according to the obtained results, the design of the building in the desired site is discussed. At the end, in order to check the amount of energy absorbed in the building, energy diagrams will be obtained for the thermal region of the coldest day of the year. The results show that the most optimal energy consumption of the residential building is related to the design of plan B with the fabric gains value of 41767 Wh. After that, the designed plan A show the most optimal energy consumption in the building with fabric gains value of 41028 Wh in the month of July. The results of this research are useful for energy ‎efficiency of residential buildings and environmental management in future.

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 score0.047
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueIranica Journal of Energy and EnvironmentSame topicSustainable Building Design and AssessmentFrench-language works237,207