Modelling Multi-level Power Usage with Latent States and Smooth Functions
Bibliographic record
Abstract
We develop and apply a new approach for analyzing a building's business day power usage. We treat each business day as a replicate and model power usage as arising from two smooth functions, one function giving power usage when the cooling system is off, the other function giving power usage when the cooling system is on. The condition chiller on/chiller off at any particular time cannot be observed directly, thus forming a latent process. In general, our method can be applied to multi-curve data where each curve is driven by a latent state process. The state at any particular point determines a smooth function. Thus each curve follows what we call a switching nonparametric regression model. We develop an EM algorithm to estimate the parameters of the latent process and the function corresponding to each state. We also obtain standard errors for the parameter estimates of the state process. Simulations studies show the frequentist properties of our estimates.
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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 | 0.001 | 0.001 |
| 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".