Thermal comfort-constrained nonlinear operational optimization of a solar-absorption-radiant cooling system
Bibliographic record
Abstract
• A thermal comfort-constrained NLP-based optimal controller is developed. • The solar-absorption-radiant cooling system has a solar fraction of 0.8. • Stricter comfort constraints increase the cost and emissions by 30.96% and 37.5%. • Doubling the ventilation rate increases daily operating costs by 19%. • The solar-assisted system saves 45.9% on costs and reduces emissions by 52.5%. With the increasing demand for sustainable building solutions, especially under extreme weather conditions, there is a growing need for renewable-powered cooling systems that can minimize energy consumption and carbon emissions. Solar-absorption-radiant cooling systems offer a promising alternative to traditional air conditioning systems, but their effectiveness relies on efficient control strategies. This study investigates the optimal control of a solar-absorption-radiant cooling system for a single-story office building using non-linear programming (NLP) to minimize operating costs while maintaining thermal comfort. This is achieved by directly integrating the building model and thermal comfort calculations within the optimization procedure. By incorporating a solar collector, storage tank, assisting boiler, and absorption chiller, the system achieves a solar fraction of 0.8, minimizing daily operating costs to 2.11 USD and carbon emissions to ∼ 39.1 k g C O 2 . The system maintains an average PMV of 0.14, an operative temperature of 25.63 °C, and a coefficient of performance of 0.72. The study also explores the impact of varying thermal comfort constraints, ventilation rates, and inlet air temperatures on system performance. Stricter comfort constraints (PMV=-0.2 to 0.2) increase costs and emissions by 30.96 % and 37.5 % respectively, due to increased reliance on the natural gas boiler. Doubling the ventilation rate based on fresh outdoor air increases daily costs and emissions by 19 % and 22.6 % respectively. Conversely, utilizing a supplementary system to supply ventilation air at 25 °C significantly reduces costs and emissions by 26.2 % and 25.4 % respectively, and increases the solar fraction to 0.92. Compared to a conventional system powered solely by a natural gas boiler, the solar-powered system achieves substantial cost savings (45.9 %), reduced carbon emissions (52.5 %), and improved thermal comfort, highlighting the potential of this technology for sustainable building operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".