How to enhance the energy efficiency of HVAC systems
Bibliographic record
Abstract
HVAC systems for controlled microclimate environments can be very energy consuming depending on the specific thermo-hygrometric conditions to be attained with respect to the conditions of external air.The aim of the present work is the analysis of some useful strategies devoted to reduce HVAC energy consumption.To this end, an in-house developed software is employed to evaluate energy consumption in terms of heating/cooling, humidification/dehumidification and reheat contributions.The test-case considered in this work is a museum environment where specific conditions are required to ensure long-term safety and preservation of the exhibitions by considering also visitors occupancy.The HVAC system is coupled with the exhibition room where the energy demand is computed through the microclimate energy balance.The simulation model has been validated by comparing the results in terms of energy demand to those obtained in the literature.Different strategies have been implemented to reduce the energy cost, i.e. a partial by-pass of cooling coil, an increase of the accepted tolerance for the relative humidity set-point value, an improvement of the thermal insulation of the building and a recirculation of the microclimate air.The results obtained by adopting such strategies have been compared to assess the efficiency of HVAC systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".