Insight Into Predictive Models: On The Joint Use Of Clustering And Classification By Association (CBA) On Building Time Series
Bibliographic record
Abstract
Data-driven, black box machine learning models have received a lot of attention in the field of building control. They have been used successfully to predict building behaviour given information like weather forecasts and real time sensor information. In these models, the occupant behaviour is considered to act exogenously on the building. We consider the users as active elements of the building operation control loop. To make educated control decisions they have to be informed about how the building will behave. Therefore, we propose a prediction model which explains to occupants the dayahead building behaviour using a clustering and classification by association model. We benchmark this approach to a neural network regression model and only observed a small loss of accuracy. Knowing the upcoming building behaviour, occupants can adjust their behaviour (e.g. putting on clothes) or the building systems settings (e.g. set points) accordingly. The proposed method is a promising way to decode complex regression models into readable rules, which in future may be useful in conjunction with for example voice-based virtual assistants.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".