A Distributed and Scalable Approach to Semi-Intrusive Load Monitoring
Bibliographic record
Abstract
Non-intrusive appliance load monitoring (NIALM) helps identify major energy guzzlers in a building without introducing extra metering cost. It motivates users to take proper actions for energy saving and greatly facilitates demand response (DR) programs. Nevertheless, NIALM of large-scale appliances is still an open challenge. To pursue a scalable solution to energy monitoring for contemporary large-scale appliance groups, we propose a distributed metering platform and use parallel optimization for semi-intrusive appliance load monitoring (SIALM). Based on a simple power model, a sparse switching event recovering (SSER) model is established to recover appliance states from their aggregated load data. Furthermore, the sufficient conditions for unambiguous state recovery of multiple appliances are presented. By considering these conditions as well as the electrical network topology constraint, a minimum number of meters are obtained to correctly recover the energy consumption of individual appliances. We evaluate the performance of both SIALM and NIALM with real-world trace data and synthetic data. The results demonstrate that with the help of a small number of meters, the SIALM approach significantly improves the accuracy of energy disaggregation for large-scale appliances.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".