Resilient Event Detection Algorithm for Non-Intrusive Load Monitoring Under Non-Ideal Conditions Using Reinforcement Learning
Bibliographic record
Abstract
Event detection is critical in a non-intrusive load monitoring (NILM) solution. NILM is essential for the implementation of some demand-side management (DSM) techniques. This article proposes an event detection algorithm based on reinforcement learning for NILM purposes (RLNILM). The proposed method employs a number of simpler traditional event detection algorithms, e.g., LLR voting, or SWDC, to train the RLNILM agent through a feedback system that separates the RLNILM agent from directly accessing consumers' data. The performance of the proposed RLNILM method is validated using the real-world data from the iAWE dataset under ideal and non-ideal conditions. Four test scenario groups include cases where the frequency, input electric signals, or access to crucial grid information varies significantly. In all scenarios, the RLNILM agent outperforms traditional event detection algorithms used in the feedback system. The results show not only the proposed architecture increases the cyber security of the customers connected to NILM services, but also it improves the performance of real-time event detection algorithms in non-ideal grid conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".