A short-term load forecasting method for distribution networks based on multivariate information and exploratory factor analysis
Bibliographic record
Abstract
Accurate short-term load forecasting of distribution networks can ensure the normal life and production of the society, effectively reduce the cost of power generation, and improve the economic and social benefits.Aiming at the multivariate information that affects the power load, this paper utilizes factor analysis to reduce the dimensionality of the original influencing factors, and obtains the main influencing factors with the highest contribution rate, so as to guarantee the accuracy of the neural network prediction.On this basis, the neural network structure is improved by combining AlexNet and GRU, and the short-term load prediction model of distribution network is finally constructed.The relevant charge data of N village in 2023-2024 is used as a research sample to analyze the main influencing factors of its short-term load change, and three main influencing factors affecting the load change in the short term are identified as temperature, air pressure, and humidity factor.Based on the real data of N-village distribution network to carry out prediction simulation experiments, the load short-term prediction curve of this paper's model has a better fitting degree and good stability, and the values of the prediction result evaluation indexes MRE, RMSE and MAE are smaller than those of the other comparative models, which are basically able to maintain a prediction accuracy of more than 90%.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".