COVID-19 impact on wind and solar energy sector and cost of energy prediction based on machine learning
Bibliographic record
Abstract
This study examines the impact of the COVID-19 pandemic on renewable energy sectors across seven countries through techno-economic analysis and machine learning (ML). In China, the renewable fraction decreased in grid-connected systems due to 14.6 % higher diesel fuel prices. They reduced grid electricity prices, with Cost of Energy (COE) reductions driven by a 2.8 % inflation decrease and a 3 % discount rate cut. The increase in renewable energy adoption in the USA during the pandemic was driven by decreased initial and operational costs of renewable components, a significant rise in diesel fuel prices, and government policy changes, despite a reduction in renewable energy sell-back prices and rising capital and annual costs due to expanded renewable capacity. Canada noted a shift to standalone systems with 50 % lower PV sell-back prices, 2 % lower WT prices, and a 48 % fuel cost rise, reducing COE except in grid/WT scenarios. Germany managed rising electricity and fuel costs, decreasing COE despite inflation. India expanded standalone HRESs driven by a sevenfold PV capacity increase, lowering COE. Japan saw stable COE with minimal variation. Iran faced economic challenges with a 104 % inflation increase, impacting COE despite a grid-connected COE decrease. Machine learning forecasts suggest that COVID-19 may cause an increase in COE in China and India due to pandemic effects.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".