A Study on Markovian and Deep Learning Based Architectures for Household Appliance-level Load Modeling and Recognition
Bibliographic record
Abstract
The promise of non-intrusive approach of Appliance Load Monitoring (ALM) promotes the load decomposition analysis at the most disaggregated level. Accordingly, appliance-level load modeling is bolstered to provide appliance-level information and quantify energy consumption. This paper intends to investigate the proficiency of Markovian models, as the state-of-the-art and Deep Learning (DL) architectures, as the cutting-edge of machine learning methods for load modeling through disaggregation practice. Particularly, a simple Recurrent Neural Network (RNN) as a fundamental network architecture for DL is chosen, which is consistent with first-order Markovian chain assumption. A dataset with a challenging load disaggregation case is utilized for the analysis. The same learning mechanism is used to execute the training phase of both approaches, regarding a fair performance comparison. Consequently, the recognition accuracy of the algorithms is evaluated. The results demonstrate that Markov decision procedure is comparable with DL basic manner. Additionally, the paper elaborates remarks on essential prerequisites, specifically data adequacy, to provide a thorough load modeling analysis. From a practical standpoint, this work aims to pinpoint major barriers in terms of both load model construction and recognition towards actual implementation.
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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 | 0.000 | 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".