A spatial–temporal data-driven deep learning framework for enhancing ultra-short-term prediction of distributed photovoltaic power generation
Bibliographic record
Abstract
Effective utilization of spatial-temporal information can improve the accuracy of short-term prediction of power generation from distributed photovoltaic (PV) stations in the region. This paper introduces a short-term spatial-temporal prediction model for distributed PV power generation, blending data-driven methodology with deep learning technique. The model integrates a self-attention mechanism (SA), a Bi-directional Convolutional Gated Recurrent Unit (BiConvGRU), and an encoder-decoder structure, called ABCGRU. The spatial-temporal attributes of PV power generation can be effectively utilized to accurately predict the output of PV power stations at different locations. Firstly, this paper proposes a 2D distributed PV measurement frame approach considering the spatial-temporal properties of PV power. The combination of Pearson's correlation coefficient, Euclidean distance, the cross-correlation-based SBD and geographic distance reduces the input dimensionality. Secondly, to better capture the spatial-temporal patterns within the 2D distributed PV measurement frame, this paper proposes the ABCGRU model. Finally, the predictive performance of the model is verified through experiments. On the Birmingham dataset, the mean squared error (MSE) of single-step (15 min) prediction is 4.36, and the mean absolute error (MAE) of multi-step (30 min-60 min) prediction is 1.66. The average MSE of single-step prediction for the Little Rock and New Orleans datasets is 5.67. In the comparison between the same series of models, the 4-layer ABCGRU has the highest accuracy. The normalized mean absolute error of the single-step prediction for the Datong data is 0.036.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".