Thermostat control strategies for thermal comfort and energy-efficient operation
Bibliographic record
Abstract
Thermal comfort standards, such as ASHRAE Standard 55, prescribe a thermal comfort zone based on operative temperature, which includes both dry-bulb temperature and mean radiant temperature in the calculation. However, the built environment, regulated by a dry-bulb temperature based thermostat, will quite often maintain a space outside of its thermal comfort zone. The problem is more pronounced in a space open to a large window surface when the influence of the mean radiant temperature is not considered. The objective of the research is to develop control strategies that can maintain the space within the thermal comfort zone and offer energy savings. \nThe work is based on energy modeling and simulation of the five thermal zones (core, south, east, north, and west) of a prototypical small office building for four different climate zones (represented by Miami, San Diego, New York, and Montreal) under the influence of various window sizes (ranging from window-to-wall ratios of 10% to 80%). EnergyPlus software is used to simulate indoor environmental conditions and calculate the energy demand to maintain such conditions. Fanger's Predicted Mean Vote (PMV) model is used to estimate occupants’ thermal sensations in the space operated under the control of conventional dry-bulb thermostats. A performance indicator is developed to facilitate the calculation of new thermostat setpoints (and subsequent development of control strategies) that could ensure the space is maintained within the thermal comfort zone and operated with some energy savings for all occupied hours. \n\tSimulation results reveal a monotonic correlation between window size and the number of hours outside of the thermal comfort zone. The proposed control strategies are not only able to maintain the space within the thermal comfort zone and offer energy savings but also can be implemented with no modification to existing HVAC equipment or building fabric.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".