A STUDY ON A THERMAL LOAD SIMULATION METHOD FOR SPACES CONDITIONED BY HEATING, COOLING AND FRESH AIR CONTROL SYSTEMS COMBINED WITH NATURAL VENTILATION
Bibliographic record
Abstract
This paper presents a thermal load simulation method for spaces conditioned by heating, cooling and fresh air control systems combined with natural ventilation. Fresh air control systems enables free cooling, demand control ventilation and energy recovery ventilation. Natural ventilation may be carried out while spaces are conditioned by cooling equipment. For simulation of natural ventilation, many conditions which must be satisfied for natural ventilation can be considered and the under limit of space air temperature is supposed to be controlled by regulating ventilation opening area. On the other hand, natural ventilation rate is calculated under the simple conditions where airflow balance is not solved and the location of the neutral zone is assumed to be fixed. Space air temperature control by free cooling and heat recovery ventilation can be simulated. In demand control ventilation, ventilation rate is estimated in proportion of occupancy rate For heat balance simulation of multi zones where fresh air control systems combined with natural ventilation are used, a new method was developed in order to enable speedy simulation and to avoid complicated procedure. In the proposed method for iterative solution of heat balance and fresh air rate which means sum of natural ventilation rate and forced ventilation rate for free cooling, space air temperature control by regulating fresh air rate is replaced for control by a virtual heater and cooling and heating capacities are intentionally increased. This procedure suppresses iteration of calculating fresh air rate and the solution such as heating rate, cooling rate and fresh air rate can be obtained by correcting the calculated results. The proposed simulation method was applied to the building simulation engine in BEST which is a whole building energy and thermal load simulation program and the simulations for a typical office building located in Tokyo were performed. The basic effects of fresh air control techniques and natural ventilation on space thermal environment and thermal load were presented. The interaction effects of energy saving strategies such as high performance building facades, natural ventilation and fresh air control techniques were evaluated through simulations in two buildings. One is a low-performance building and the other is a high-performance building. The energy saving rate for a low-performance building is defined as the thermal load reduction achieved by adding an energy saving strategy, and that for a high-performance building is defined as the thermal load increase resulted by removing the strategy employed in the high-performance building. Although some strategies provide lower energy savings for the high-performance building than for the low-performance building, a few strategies are more effective for the high-performance building.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".