Hybrid Renewable Energy Resources Selection Based on Multi Criteria Decision Methods for Optimal Performance
Bibliographic record
Abstract
This paper used some Muti-Criteria Decision Analysis (MCDA) techniques to select the best alternative renewable energy sources in Msallata city, south east of Tripoli, Libya. They were based on the commitment from the ministry of Energy in the Libyan government to lower their carbon footprint. The renewable energy sources considered here are solar, wind, and biomass. MCDA is widely used to solve various decision problems through alternative evaluation. MCDA methods are currently applied in every field and can define any problem, alternatives, and criteria. However, every MCDA technique can give different results. In this paper, four MCDA methods have been tested and evaluated based on the renewable energy sector to find the best alternative. The results suggest that a combination of wind and solar is the most important energy source; solar plants alone are the second most important energy source. The least important energy source in this model is biomass alone. This work is validated using HOMER Pro Software. Many MCDA techniques are applied these days in almost all disciplines, but they may have different results. This work proved that the best MCDA for dealing with renewables in our case is either The COmplex PRoportional ASsessment (COPRAS) or VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). COPRAS is a MCDA technique that is developed by Zavadskas, Kaklauskas, and Sarka in 1994, it is applied to maximiza and minimize index values. VIKOR is an abbreviation of a Serbian term that means Multicriteria Optimization and Compromise Solution, it ranks and selects from various alternatives with conflicting criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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 teacher head, 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".