Energy Disaggregation using Multilabel Binarization and Gaussian Naive Bayes Classifier
Bibliographic record
Abstract
Nearly everywhere across the globe, energy demand is increasing, as new residential buildings emerge and population growth continues. It is important to meet the energy demand while still generating less energy to make up for what is needed and energy disaggregation can assist in this process. In this work, we propose an energy disaggregation method that would allow consumers to be informed on the state (on or off) of their appliances at any point in time. This would enable them to adjust their appliance usage thereby consuming less energy. Our disaggregation approach can disaggregate any combination of appliances among a set of appliances. This is done by creating and fitting n-models for the n-combinations possible in a strategic manner. Prior to the models, Multilabel Binarization has been used to deduce the labels of the data, then the models are developed and fitted using Gaussian Naive Bayesian Classifier. Thereafter, with Jaccard Coefficient per appliance and accuracy, recall, precision and f1-score were used for overall score. We evaluate these models with the original and noisy synthesized test data, resulting in an average overall F1-score of 91% for the original and 87.8% for the synthesized test data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".