Bayesian-Optimized CNN-LSTM for Forecasting Short-Term Photovoltaic Power in Microgrids
Bibliographic record
Abstract
The development of deep learning algorithms has also made it possible to use artificial neural network algorithms to forecast time series with excellent results because solar energy is intermittent. In this research, a CNN-LSTM structure optimized using Bayesian optimization is suggested for photovoltaic power forecasts of the Rye microgrid employing real data coming from an area in Trondheim, Norway. The proposed hybrid model extracts local characteristics from the data by using the convolutional structure as a filter, as well as the construction of short- and long-term memories, and then extracts temporal features. In a hybrid prediction technique, the method updates hyperparameter weights using Bayesian inference. According to the performance evaluation, the suggested model performed better than the others, obtaining an MAE of 1.38 and an RMSE of 3.93. Ultimately, the hybrid model’s performance was evaluated against that of ANN, MLP, CNN, LSTM, and GRU. Additionally, Results show that, concerning the prediction effect, the proposed hypothesis performs better than the other models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".