Power Utilities Energy Data Acquisition and Forecasting: GUI Development and ML Evaluation
Bibliographic record
Abstract
Electric load forecasting is critical for power system planning and operation, especially given the rising electricity consumption and integration of renewable resources. Machine learning (ML) is widely used for energy forecasting, but the lack of consistent and complete historical data can hinder ML model research, as fragmented and flawed datasets may degrade model performance. This study presents a graphical user interface (GUI) to access reliable historical load data from power utilities, aiding in ML model development. The developed GUI was tested for retrieving data from two utilities—New York Independent System Operator (NYISO) and Hydro-Québec (HQ). Four ML models—linear regression, random forest, eXtreme gradient boosting, and long short-term memory—were used to evaluate data quality and forecasting performance. Results show high prediction accuracy, with mean absolute percentage error (MAPE) typically below 5% and coefficients of determination (R2) close to 1. This study underscores the GUI's effectiveness in simplifying data collection while highlighting the importance of data availability and quality in achieving accurate results.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".