A Model-Based Short-term Load Forecast Methodology for Aggregated Power Consumption of Thermostatically Controlled Appliances in DSM
Bibliographic record
Abstract
One of the challenges of integrating renewable resources such as wind and solar energy into the existing electric power system is their rapid fluctuations that tends to decrease the reliability of the grid. Direct load control is one method to mitigate this variability and thermostatically controlled appliances such as air conditioners can play a significant role due to their large share to the total load. However, the system operator requires a reliable estimation about the magnitude of this load and how much it can be shifted. This paper presents a model-based forecasting method to provide a short-term load forecast of the controllable load. The main benefits of this method are 1) using real-time measurements to build a model of system; therefore, there is no need for a large historical measurements nor physical parameters, 2) fast response to the sudden changes in the system parameters. The performance of the proposed method was evaluated using a numerical simulator that shows it can generate forecasts with less than 6% error.
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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) | 0.000 | 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 | 0.001 | 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 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".