Non-Intrusive Load Monitoring Using Machine Learning Accelerator Hardware for Smart Meters
Bibliographic record
Abstract
Residential electricity customers consume a significant amount of energy due to the extensive use of inefficient appliances. In order to save energy and reduce the electricity bills of the customers, load monitoring provides such customers with information to make informative cost-effective decisions. Non-Intrusive Load Monitoring (NILM) determines which appliances are on at the electrical input to a residence. Machine Learning (ML) based methods of NILM offer flexibility at the cost of computational complexity. This paper investigates addressing the problem of the computational bottleneck using a novel ML-based acceleration hardware. In this work, ML is used to develop a NILM algorithm, which is then tested on a publicly available dataset named the Reference Energy Disaggregation Dataset (REDD). Subsequently, a physical system modelling an end-to-end smart metering solution is designed and tested. The results show a significant decrease in the time and energy required to run the ML algorithms and most importantly, the successful real-time operation of NILM algorithms embedded in a smart meter. By using the newly developed ML acceleration hardware and ML-based algorithms, NILM can be embedded into next-generation Smart Meters.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".