A Simulation-Optimization Model for Solar PV Panel Selection Under Solar Irradiance and Load Uncertainty
Bibliographic record
Abstract
In this reserach, a multi-objective model is presented considering simulated behavior of high-efficiency rooftop solar PV panels in factory, which are among the largest producers of green-house gases. The paper proposes a simulation-optimization approach is used to maximize the net present value (NPV) of economic benefits along with minimizing the payback period (PBP) of the investment, and maximizing solar energy consumption rate (SECR). In addition, the solar PV panels degradation and maintenance cost, as well as the uncertainty in solar irra-diance and demand load, are also considered. The study consists of two scenarios, in the first of which both electricity tariffs and feed-in-tariffs (FiT) are fixed by a long-term contract. The second scenario investigates the situation in which subsidies on electricity tariff are removed. The best type of panels are found in each scenario considering trade-off between objective functions. The preferred trade-off solution in the first scenario, with 2% increase in PBP, achieves more than 10% growth in NPV which is about $15000 in a year. In the second sce-nario, with only about 0.2% decrease in NPV and 3% increase in PBP, the preferred solution attains 9% increase in SECR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".