Non-Intrusive Monitoring Systems for the Refinement of Energy Analysis Models in Public Buildings
Bibliographic record
Abstract
The influence of occupant behavior and improper management practices on energy consumption in buildings is becoming increasingly significant with the decrease in the overall energy demand. The recent jump in smart meter technology opened new perspectives in monitoring occupant behavior and the poor management of air conditioning systems in buildings, allowing them to be used to refine energy consumption analysis and forecasting models. This paper describes the research activities carried out within the RSE 2024-25 High efficiency buildings for energy transition, aimed at implementing experimental data for monitoring indoor comfort in two public buildings in the municipality of Carbonia. Targeted measurement campaigns were conducted to evaluate electricity consumption in some residential and two public buildings. Primary energy consumption was instead estimated: for residential buildings, from sample interviews; for public buildings from municipal administration billings and systems’ operating hours. The hourly data provided by Indoor Air Quality sensors allowed the validation of the results obtained. The results show that the most significant errors (over 42%) are in estimating the primary energy requirement for summer air conditioning, largely due to unrealistic efficiency data on the generation systems. The data have been correlated to the different construction typologies and related to the territorial energy mapping of interest.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".