Evaluation of Two Novel Supervised Non-Intrusive Load Monitoring Techniques
Bibliographic record
Abstract
Non-intrusive Load Monitoring (NILM) is a critical tool in energy management which allows disaggregating total electricity consumption into individual appliance-level energy consumption within a household or commercial setting, all without the need for intrusive monitoring devices. NILM empowers consumers, utilities, and researchers to gain a deeper understanding of how electricity is utilized, aiding in energy conservation, billing, and load optimization. This paper introduces two pioneering supervised NILM models designed to enhance load disaggregation accuracy and versatility in both tertiary and residential environment. The first model presented in this research is a machine learning model which employs a unique fusion of Bayesian optimization technique over ensemble learning approach. The Machine Learning based ensemble approach leverages the complementary strength of six different regressor techniques and the Bayesian optimization technique optimizes various parameters of the regressors. Thus, this proposed machine learning model effectively enhances the NILM system's adaptability to various electrical environments and scenarios. The second model adopts a Deep Learning technique based on Temporal Convolutional Network architecture for NILM regression task by efficiently capturing temporal dependencies of the sequential NILM data. The models were evaluated based on two energy consumption data collected from a tertiary (university laboratory) and a residential setting. The preliminary results of the novel approaches indicate substantial improvements in load disaggregation accuracy when compared to recent benchmarking NILM techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".