Assessing Distributed Solar Power Generation Potential under Multi-GCMs: A Factorial-Analysis-Based Random Forest Method
Bibliographic record
Abstract
The development of renewable energy is important for climate change mitigation and socioeconomic sustainability, and the prediction of renewable energy potential (e.g., solar) under the consideration of climate change impact is challenged. In this study, a factorial-analysis-based random forest (FARF) method is developed for the distributed solar power generation (DSPG) predication under multiple global climate models (GCMs). FARF has advantages in (i) downscaling large-scale climate variables to local scales, (ii) avoiding the problem of overfitting in traditional models; and (iii) reflecting the main and interactive effects of climate variables on solar radiation intensity (SRI). Then, the FARF method is applied to the Jing-Jin-Ji region of China to predict the DSPG potential under three GCMs and two emission scenarios (RCP4.5 and 8.5). Multiple validation coefficients prove that the FARF method is effective and feasible. Major findings are as follows: (i) during 2021–2100, the regional SRI would increase under all GCMs and RCPs, and the southern region is obviously higher than the northern region; (ii) the main impact factors are temperature (contribution >51%) and humidity (contribution >28%), and the interactive effects of multiple factors are insignificant; (iii) the regional DSGP would continuously rise and its contribution to electricity consumption would continue to increase; and (iv) under all GCMs, SRI and DSPG under RCP8.5 would be higher than those under RCP4.5. The findings can help decision makers to use the desired strategies for promoting renewable energy utilization and energy system sustainable development.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".