Non-Intrusive Load Monitoring-based Fuzzy Actor-Critic Reinforcement Learning Home Energy Management
Bibliographic record
Abstract
Energy management is economically and environmentally crucial for today’s modern life. This paper presents a home energy management system (HEMS) using fuzzy actorcritic reinforcement learning (FACRL) and non-intrusive load monitoring (NILM), which follows a cyber-physical system (CPS) structure. An LSTM-based neural network predicts future residential consumption using the NILM dataset. The Python-based NILM toolkit disaggregates the whole power into appliancelevel power to obtain the consumption pattern of non-shiftable appliances. Then, the FACRL algorithm schedules the power usage of controllable residential facilities. To the best of our knowledge, it is the first time that the FACRL method has been applied to design a HEMS. The proposed method is applied to manage the consumption pattern of a house in Ottawa according to time-of-use tariffs and the temperature of this city, as well as the comfort level of the user. The results demonstrate low consumption during on-peak hours and the economic benefits of the HEMS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".