Solar Energy Forecasting Using Statistical, Machine Learning, and Deep Learning Approaches
Bibliographic record
Abstract
Solar energy is one of the vital Distributed Energy Resources (DERs) for creating a Virtual Power Plant (VPP). However, its integration into the electricity grid is challenged by its intermittent nature due to fluctuating weather conditions. Thus, Solar energy forecasting is crucial to ensure a stable and reliable power supply. This paper performs solar energy forecasting using statistical, machine learning (ML), and deep learning (DL) approaches. A comprehensive analysis of existing literature reveals a gap in comparative studies and real-world implementation considerations. In this paper, these problems are addressed by providing a system design and modeling framework that incorporates real-world challenges such as data delays, feature selection adaptability, etc. Solar power generation was forecasted 3 hours ahead and the performance of different algorithms was evaluated including investigating the underlying reasons for their effectiveness. Results indicate that machine learning models, particularly Random Forest, outperform others with the lowest RMSE and MAE, while statistical models exhibit the least accuracy. The study was concluded with recommendations for practical implementation and directions for future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".