Optimisation du design preliminaire des systemes cvca
Bibliographic record
Abstract
HVAC (heating, ventilation and air conditioning) systems are recognized as one of the biggest energy consumers in commercial and institutional buildings. Generally, designers use common sense, historical data and subjective experience in designing these systems: such designs must take into account the way in which the zones served by the systems are grouped as well as the number of systems that serve each building. HVAC energy efficiency is not an easily calculable criterion to use when selecting such systems; usually, the lowest investment price becomes the primary selection criterion and not the life-cycle cost. The grouping of the zones served by the systems and the number of systems serving the building are the variables during the iterative optimization process. This thesis outlines a first optimization method for preliminary HVAC systems design using an evolutionary algorithm that uses reduction in energy consumption, as the optimization criterion. DOE-2 software was used to calculate the objective function, that is to say, HVAC energy consumption. The use of a calculation engine such as DOE-2 requires the involvement of a simulation expert in order to construct the building's model as well as to solve any problems encountered during simulation. As this problem is rather complex, we suggest to modify the first method by introducing a simplified model based on the zones' daily profile loads: essential data in the preliminary phase of design. A global load ratio (GLR), described as the ratio between the system's real load and its possible maximum load for a given period, is applied as objective function. The optimization variables are: (i) grouping of the zones served by the systems and (ii) number of systems serving the building. Constraints have been selected in order to ensure accurate representation of the variables' limits. The two optimizations methods are applied on a real building to see the impact on the energy consumption. The results we obtained during preliminary HVAC optimization design process could prove to be very useful for engineers during the preliminary design phase. The comparison between the reference building and the optimized building (under identical constraints) yielded significant energy savings for HVAC energy consumption. These savings are dependent upon the building's configuration, type of constraints imposed, types of HVAC systems it has, and the control strategies these systems employ. Keywords: global load ratio; HVAC systems; grouping of zones; optimization; evolutionary algorithm; energy consumption; simulation; DOE-2; design; ASHRAE.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".