Residential Power Forecasting Using Load Identification and Graph Spectral Clustering
Bibliographic record
Abstract
Forecasting energy or power usage is an important part of providing a stable supply of power to all customers on a power grid. We present a novel method that aims to forecast the power consumption of a single house, or a set of houses, based on non-intrusive load monitoring (NILM) and graph spectral clustering. In the proposed method, the aggregate power signal is decomposed into individual appliance signals and each appliance's power is forecasted separately. Then the total power forecast is formed by aggregating forecasted power levels of individual appliances. We use four publicly available datasets (reference energy disaggregation dataset, rainforest automation energy, almanac of minutely power dataset version 2, tracebase) to test our forecasting method and report its accuracy. The results show that our method is more accurate compared to popular existing approaches, such as autoregressive integrated moving average, similar profile load forecast, artificial neural network, and recent NILM-based forecasting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".