Machine Learning-Based Demand and PV Power Forecasts
Bibliographic record
Abstract
In recent years, predicting the performance of the power system is of high-priority to alleviate potential uncertainties. Achieving accurate load prediction at the customer and at the renewable energy resources levels will lead to more efficient balance of supply and demand. In this paper, we investigate different models including Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP) and Long Short-Term Memory (LSTM). The models were used for two tasks: predicting the Load Demand and the PV generation. For the Load Demand prediction models, the dataset considered was the aggregated load demand for 40 randomly chosen homes in Austin, Texas and the weather in Austin, Texas. For the PV generation Model, the dataset considered was the PV generation of Yulara Plant in Australia and the weather conditions for the site. The MLP model gave us the best results for the Load Prediction closely followed by the LSTM and the LSTM model gave us the best result for the PV Prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".