A Generic Load Forecasting Method for Aggregated Thermostatically Controlled Loads Based on Convolutional Neural Networks
Bibliographic record
Abstract
Thermostatically Controlled Loads (TCLs) are excellent candidates for demand response. Load forecasting of aggregated TCLs is used to help utility companies predict and manage their load demand. This paper presents a generic load forecasting method that aims at providing accurate forecast for different TCL datasets without the need of extracting specific predictors. It relies on Data Quantization and Dequantization modules to processes the inputs and outputs, and a group of Convolutional Neural Network modules used for automatic feature learning and multi-horizon load demand forecasting. The forecasting method was studied with different simulated TCLs data and aggregation sizes resulting in an enhanced performance when compared with three traditional forecasting methods. Additionally, the generalization capacity of the proposed forecasting method was studied with real data from a conference environment (building) corroborating the performance advantage of the method and the generalization capacity of the "predictor-less" approach.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".