A Nonintrusive Load Monitoring Based on Multi-Target Regression Approach
Bibliographic record
Abstract
This paper proposes an experimental design process for the application of energy disaggregation using multi-target regression, a new data learning approach in this application area. The approach shows to be a suitable model for dealing with energy disaggregation problems in which the task is to predict multiple appliances usage from the aggregate data. The experiments were conducted by analyzing AMPds2 and ECO public data sets for verifying the effectiveness of the approach. The data were analyzed through the machine learning process to select the optimal set of electrical features, learning algorithm, and model parameter so that the system resulting from the process could deliver the optimal performance for loads inference. Results of the data learning showed that the electrical features set of current (I), real power (P), reactive power (Q), and power factor (PF) for the aggregate data and Random Forest as the base regressor for multi-target regression model could provide the best disaggregation performance. The overall predictive performance of disaggregation accuracy and F-score outperformed the benchmarking Super State Hidden Markov Model (SSHMM) and Denoising Autoencoder (DAE) network approaches.
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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 | 0.001 | 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".