Residential Power Forecasting Based on Affinity Aggregation Spectral Clustering
Bibliographic record
Abstract
Power utility companies rely on forecasting to anticipate future consumption needs, plan power production, and schedule the selling/purchasing of power. We present a novel method to forecast the power consumption of a single house based on non-intrusive load monitoring (NILM) and affinity aggregation spectral clustering, with the idea of extending it to forecasting consumption in a larger set of houses like a microgrid. First, we use a graph to model statistical relationships between appliances. Specifically, the ON/OFF time-of-day and state duration probabilities are used to compute graph edge weights and establish statistical relationships among appliances. Then, leveraging on our previous work on NILM, we disaggregate the smart meter aggregate power profile into individual appliance power profiles. With the disaggregated individual power profiles and the corresponding ON/OFF time-of-day and state duration probabilities, we next propose a method to forecast each appliance's power profiles using affinity aggregation spectral clustering. For the proposed method, we incorporate human behaviour and environmental influence in terms of calendar and seasonal contexts in order to enhance the forecasting performance. Finally, the results of appliance-level forecasting are aggregated to perform house-level forecasting. To test our proposed forecasting method, we use four publicly available datasets and compare our method against several existing approaches such as autoregressive integrated moving average, similar profile load forecast, artificial neural network, and recent NILM-based forecasting. Experimentally, we examine how well the proposed forecasting method can generalize appliance behaviours from one house to another. Results clearly show that our method is more accurate than existing approaches.
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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".