Integrating Energy Efficiency and Occupancy Control in Shared Public Buildings: A Data-Driven Approach
Bibliographic record
Abstract
Environmental parameter monitoring systems connected in classic Internet of Things (IoT) networks have been evolving in the recent years and are now capable of providing massive amounts of data, that are often accessible to both facility managers and authorized users through smartphone apps.This paper presents an example of such monitoring systems that has been designed to control the environmental data within the many buildings that compose the University of Pisa, with the goal of improving their energy management.In fact, it is known that smart management of the energy system is the best strategy to avoid energy wastes.The topic has become particularly relevant following the COVID-19 pandemic, after mechanical ventilation has been imposed by law in many states.This leads to rather significant increases in energy use, both in the winter and summer seasons.We describe CO2 monitoring sensors that have been developed at the University of Pisa, based on low-cost components and a secure IoT network, showing their promising potential for energy efficiency applications, also highlighting some shortcomings of currently available technology.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".