Simplified optimization method for preliminary design of HVAC system and real building application
Bibliographic record
Abstract
HVAC systems (heating, ventilation, and air conditioning) are recognized as the greatest energy consumers in commercial and institutional buildings. Generally, designers use common sense, historical data, and subjective experience in designing these systems; this includes the number of systems chosen and the grouping of the zones served by these systems. HVAC energy efficiency is not an easily calculable criterion during the selection of these systems; usually the first selection criterion is the weakest investment cost. In this article, we present a simplified optimization method for preliminary design of HVAC system using the zones’ daily profile loads. A global load ratio is applied as an optimization function. The global load ratio represents the relationship between the system's real load and its possible maximum load for a given period. The variables for the optimization problem are: (i) grouping of the zones served by the systems and (ii) number of systems serving the building. Type of system was preselected for the present study, but this could also serve as an optimization variable. The correlations between global load ratio and energy consumption were shown using an office building. Then, this method was applied on an existing institutional building and compared with the detailed optimization method. In the second method, the HVAC energy consumption, calculated using DOE-2 software, was used as the optimization function. The comparison made among the existing, reference, and optimized buildings (all sharing the same constraints) have yielded significant energy savings for HVAC energy consumption. A life cycle cost analysis has also been done to estimate savings in terms of investment, operation and maintenance costs. These savings depend upon building configuration, the constraints imposed, the types of HVAC systems selected, and the control strategies for these 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".