A Smart Roller Shutters Control for Enhancing Thermal Comfort and Sustainable Energy Efficiency in Office Buildings
Bibliographic record
Abstract
This work focuses on the impact of different types of glazing and the dynamic control of shading using roller shutters on the thermal comfort and energy consumption of office buildings. Shading systems control is based on solar radiation and outdoor temperature during the winter period adapted to the Algerian climatic context. The main objective is to evaluate the efficiency of different control strategies in reducing heating demands and CO2 emissions. The research was conducted experimentally and numerically using TRNSYS 17 (Transient System Simulation Program). A validation was done of the prototype office building and then a parametric study aimed at verifying the influence of various parameters, including glazing type, climate, and the proposed shading scenarios based on temperature or solar radiation on both energy demand and thermal comfort. Different scenarios were proposed to reduce energy consumption and environmental impact. The obtained results demonstrate that shading systems are beneficial even in winter and highlight the effectiveness of controlling shutters based on solar radiation compared to temperature control for the different studied regions for a standard building. This approach achieves reductions of up to 21% for energy consumption, along with a significant decrease in carbon footprint, contributing to the sustainability of energy management in office buildings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".