A Regression Model-Based Short-Term PV Power Generation Forecasting
Bibliographic record
Abstract
Solar photovoltaic (PV) modules have been increasingly integrated into power systems. However, their intermittency and variability have considerable impacts on power grids and could jeopardize the grid's stability when the penetration is high. Developing accurate PV power generation forecasting methods is key to enhancing reliable and secure grid operation. In this paper, a data-driven regression model-based short-term PV power generation forecasting is proposed, where nineteen regression models (including both deterministic and probabilistic predictors) from five regression families are evaluated, and performance assessment indices, such as RMSE and R-squared, are adopted to find the best models. To further improve the performance of forecasting models, hyperparameter optimization and tuning are conducted using MATLAB Regression Learner App. A real-world historical dataset of PV power generation is used to train and further test the models. It is found that the interactions linear, medium Gaussian support vector machine (SVM), and the ensemble of bagged trees outperform other regression models in this study. The proposed method can be utilized by the system operator for effective scheduling future power systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".