Assessing the Efficiency of Photovoltaic Panel Implementation in Riyadh's Residential Sector through Life Cycle Cost Analysis
Bibliographic record
Abstract
In recent decades, Saudi Arabia has seen remarkable growth, bringing along a wave of new opportunities. This surge has led to a population boom in major cities, driving up the demand for energy across the Kingdom. In line with its Vision 2030 plan, which aims for a more diverse economy and society, Saudi Arabia is keen on reducing its reliance on fossil fuels at home. And with plenty of local resources available, the idea of making our own solar components seems quite doable. But with more people using more energy, especially in homes where air conditioning is a big part of our lives, we need to find smarter ways to power our homes. Solar panels seem promising, but we're not sure if they're actually more cost-effective than our current electricity sources. So, we're embarking on a study to figure that out. We're going to compare the costs of using solar panels versus traditional electricity in Riyadh. We'll be looking at two similar residential buildings, considering things like their construction, size, and how much electricity they use. One of these buildings will get fitted with solar panels, while the other will stick to regular electricity. Then, we'll crunch the numbers and see which one ends up costing less over time. If solar panels turn out to be the cheaper option, it could be a game-changer, encouraging more people to go solar and make our energy consumption a bit greener.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".