Smart Thermostat Use in Multi-unit Residential Buildings: Impacts on Occupant Behaviour, Thermal Comfort, and Energy Performance
Bibliographic record
Abstract
As the proportion of the population who resides in urban centers grows, the number of dwellings in high-rise residential buildings is also increasing. Heating, ventilation, and air conditioning (HVAC) typically accounts for over half of all energy use in this building type and, as such, consideration of these loads is key to effective energy demand-side management. Connected, smart thermostats provide new opportunities for energy management in high-rise residential buildings by facilitating access to suite-level data for building operators and researchers, improving occupant interfaces, and enabling the implementation of new in-suite HVAC control strategies (e.g., occupancy-based and load shifting control, as explored here). In this thesis, the impacts of smart thermostat use on occupant behaviour, thermal comfort, and energy performance were investigated through a field study in two contemporary high-rise residential buildings in Toronto, Canada. The key findings for each of these areas are summarized below.1. Occupant Thermal Comfort and Behaviours: Over-conditioning of suites occurred frequently in both buildings, despite the presence of in-suite temperature controls, resulting in thermal discomfort for occupants. As compared to conventional, programmable thermostats, higher schedule programming rates were observed amongst the study population, who has smart thermostats installed in their suites. 2. Thermostat Control Strategies:Occupancy-based thermostat control was estimated to reduce suite-level HVAC use by 5.9% ± 46% on average in the studied suites, however the energy impacts were limited by pre-existing short terminal HVAC unit runtimes (average runtime of 5 minutes/hour in the heating season and 12 minutes/hour in the cooling season). Overall, the load shifting strategy was ineffective, however, substantial shifting of suite HVAC load from on-peak to off-peak periods was observed in a subset of suites. 3. Chiller Plant Operation and Performance: Based on chiller plant operation data, modelling showed that reductions in building space cooling demand had diminishing returns in terms of reducing electricity use –the first 10% to 20% of space cooling demand reductions had the most impact on chiller plant electricity use. Further, using weather-based regression models and typical meteorological year forecasts for the 2040s, space cooling electricity use was forecasted to increase by 21% over 2020 levels.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".