A Parallel Bidirectional Long Short-Term Memory Model for Energy Disaggregation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-intrusive load monitoring (NILM) is an elegant solution for monitoring energy consumption. Essentially, it only requires a set of voltage and current sensors to be installed at the electrical entry point for load disaggregation. However, the main challenge of NILM is to accurately analyze the aggregate load data and determine the electrical consumption of each appliance. Recently, there have been some deep learning (DL) techniques proposed for NILM. These include deep convolutional neural networks (DCNNs), gated linear unit and residual network (GLU-Res), bidirectional long short-term memory (BLSTM), and autoencoder (AE). Generally, they can outperform some of the existing NILM models such as factorial hidden Markov model. Nevertheless, some of these DL methods cannot handle well on multi-state appliances, appliances with sparse patterns, and appliances with rapid changing patterns. This article proposes a new NILM model, which involves parallel convolution neural networks and BLSTM layers. Moreover, a feature extractor is proposed to unmask useful statistical features from aggregate signals to improve the learning capability of the network. The benchmark dataset REDD was used for testing the proposed method and the state-of-the-arts such as DCNN, GLU-Res, BLSTM, and AE. The results indicate that the proposed method can successfully outperform those methods.
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.
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 it