Thermodynamic analysis and performance enhancement of an integrated solar–geothermal polygeneration system using grey wolf optimization and LSTM-based forecasting with Monte Carlo uncertainty analysis: A case study on Tenerife Island
Bibliographic record
Abstract
Global warming and fossil fuel supply limitations highlight the need for sustainable energy options. Renewable-based systems provide a path to carbon neutrality but face reliability challenges due to intermittency. This study investigates Tenerife Island's potential for integrating solar and geothermal energy. A novel hybrid system is proposed, combining concentrated solar power, geothermal energy resources, with a system comprised of the following components: a supercritical CO₂ cycle, a lithium bromide-water absorption cooling system, a multi-effect desalination unit, a three-stage organic Rankine cycle and a proton exchange membrane electrolyzer. This system produces electricity, heating, cooling, freshwater, and hydrogen, achieving baseline energy and exergy efficiencies of 62 % and 17, respectively. The system's production rates are 7844 kW power, 4416 kW cooling, 6848 kW heating, 22.6 kg/h hydrogen, and 20.7 m 3 /h freshwater. Optimization using the grey wolf algorithm enhances the energy efficiency by 21 %, the exergy efficiency by 38 %, and the hydrogen production rate by 18 %. Solar energy forecasting employs direct normal irradiance data (2005–2024) with seq2seq long short-term memory predictions up to 2030. A forward uncertainty analysis using Monte Carlo simulations reveals that cooling capacity, exergy destruction rate, and net power production are most sensitive to fluctuations in direct normal irradiance, with coefficients of variation (CV) ranging from 4.4 % to 4.5 %, while energy and exergy efficiencies exhibit minimal coefficient of variation (CV < 0.1 %).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".