Short-term load prediction based on Pearson-optimized CNN-LSTM hybrid neural network
Bibliographic record
Abstract
Under the demand of new power system construction, it is important to establish a solid and reliable power grid structure with stable operation by accelerating the construction of a "double high" strategy with the goal of "double carbon". Bus load can reflect the operation of the power grid, so bus load forecasting is important to maintain the safety and stability of the power system. To solve the problems of low accuracy and inefficiency of existing load forecasting methods for power systems, this paper adopts a combined CNN-LSTM load forecasting model with Pearson optimization, which is machine learning combined with deep learning. Firstly, Pearson correlation analysis is used for data processing to extract the main features of load data. Then three neural networks, CNN, LSTM, and CNN-LSTM, are used for training and load prediction, respectively. The experimental results show that the load prediction accuracy of the hybrid CNN-LSTM neural network prediction model based on Pearson optimization is higher than that of CNN and LSTM alone and matches with the actual value, which is a load prediction method with higher accuracy.
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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 | 0.001 |
| 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) | 0.002 | 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".