Application of data mining in understanding the charging patterns of the hot water tank in a residential building: a case study
Bibliographic record
Abstract
Abstract Today’s buildings are adequately informative because of the wide implementation of the building automation system (BAS). In this regard, data mining (DM) tools have an excellent ability to interpret and discover interesting, unknown information from raw data collected from BAS. The present work is a case study, in which DM tools (such as clustering and decision tree) are applied to understand the operational pattern of a hot water storage tank (sanicube) used in a residential building, located in Scotland. The main objective of this study is to explore the correlation among the important features (storage tank temperature, solar collector, the operation of the sanicube heating element, etc.) in the chosen building. Interesting correlations and patterns between the solar collector, storage tank, sanicube-heating element are explored. The clustering results show the existence of different charging patterns of the storage tank and it highly influences the space heating (SH) and domestic hot water (DHW) temperature. The main inference from the result is that the charging of the storage tank is not automatic, and it is influenced by the occupancy behaviour. To maintain the temperature of the storage tank and subsequently to meet the SH and DHW demand, the operation of both solar collector and sanicube heating element (during night time) is recommended rather than operating only the solar collector.
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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 | 0.001 |
| 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".