Deep Reinforcement Learning For Peak Load Reduction In Aggregated Residential Houses
Bibliographic record
Abstract
Demand response aggregators will create a new economic value allowing the control and the management of a large cluster of residential houses. The flexibility enabled by consumers demand combined with power system incentives may create more opportunities for economic efficiency. However, aggregators face privacy problems and unavailability of a clear description of each household dynamics. In this case, achieving optimal control problem must rely on observations from various system trajectories. This study proposes a deep RL-based energy management in a cluster of houses as a continuous-learning agent. The objective is to achieve a maximum peak load reduction, while scheduling a cluster of thermostatically controllable loads and respecting the occupant-chosen temperature limits. In order to overcome the uncertainties related to houses' power consumption, a deep neural network uses the state of each house, which includes power consumption history, inner temperature, outer temperature and humidity, to predict the aggregated peak load. The considered RL technique shows good performances at reducing both the overall power consumption and the consumption during peak periods, while maintaining a given temperature.
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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) | 0.001 | 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 | 0.001 |
| 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".