Parametric Analysis of Daylight and Solar Energy Performance of Oasis Building Environments Through Kinetic Shading Devices Integrated with a Photovoltaic System
Bibliographic record
Abstract
Despite the growing use of parametric and computational tools in sustainable architecture, there remains a significant lack of research on kinetic shading devices integrated with photovoltaic (PV) systems.This pilot study addresses this gap by exploring the dual potential of such systems to enhance indoor daylight quality and generate clean energy in the Saharan climate of Algeria.The research targets two primary objectives, improving indoor daylight performance and maximizing solar energy production.Using Grasshopper, Honeybee, and Ladybug tools, three kinetic shading configurations were evaluated across four desert cities Biskra, El Oued, Ghardaï a, and Ouargla.Simulation results indicate that the shading devices modestly improved indoor daylight conditions, with the most effective configuration achieving a Useful Daylight Illuminance (UDI100-2000) of approximately 37% at a 0.2 opening ratio.However, the overall luminous environment remained suboptimal, as over 56% of the space experienced under-illumination (UDI<100), and Continuous Daylight Autonomy (CDA) values remained below 40%.Conversely, the integrated PV systems consistently yielded a strong energy performance, producing approximately 840 kWh/year across all models and locations.These findings demonstrate that while improvements in visual comfort were limited, the use of kinetic PV-integrated facades offers significant potential for clean energy generation in hot arid climates.Further research is recommended to conduct full optimization simulations across multiple kinetic models and building typologies to enhance the generalizability and performance of such systems.
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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 | 0.001 | 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".