Spatial-Temporal Residential Short-Term Load Forecasting via Graph Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Electric load forecasting, especially short-term load forecasting, is of significant importance for the safe and efficient operation of power grids. With the wide adoption of advanced smart meters, more attention has been paid to short-term residential load forecasting. Most of the existing load forecasting methods are mainly focused on using temporal information of historical loads, and information of neighboring houses are generally ignored. However, houses in the same or neighboring areas may show similar consumption patterns due to shared conditions such as temperature, holiday impacts. Such information can be very helpful for machine learning based forecasting methods. In this paper, we propose to tackle the short-term residential load forecasting including both the individual load and aggregated load with a graph neural network based forecasting framework. The proposed framework can capture the hidden spatial dependencies of different houses without even any prior knowledge requirement on the geographic information for these houses. The proposed framework is evaluated on data sets of different residential houses from several areas. The experimental results demonstrate that the proposed framework can improve the residential forecasting accuracy by a wide margin compared with the baselines.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it