A net-positive energy building assessment: commissioning and COVID-19 insights
Bibliographic record
Abstract
The built sector is responsible for 30% of global greenhouse gas emissions, which inspired solutions including net-zero energy buildings to reduce environmental impacts. Several factors impact energy consumption including finetuning activities that take place after construction and how occupants interact with different building components. Many building energy studies take a simulation approach to estimate energy performance. There are limited studies looking at empirical office operations, especially in Northern climates. This study investigated a case study building in Waterloo, Ontario, using quantitative energy data from three and a half years of building operation and qualitative data from key informant interviews to gain a holistic understanding of building operations. The investigation was divided in two main parts, answering questions related to the performance gap, building commissioning and COVID-19. Firstly, the difference between predictions of energy consumption from the design phase and measured energy consumption was investigated. Actions taken by the building operator to close the difference between measured and predicted heating, ventilating and air conditioning energy consumption and work towards meeting the design intent were analyzed. In the second portion of the study, the focus shifted towards more occupant impacted loads such as lighting and plug loads (e.g., computers, fridges, personal space heaters). Energy consumption from 2019 was considered as the baseline and it was compared to minimal occupancy in 2021 and medium occupancy due to increased remote working during 2022. Statistical analysis was completed to test the significance of the differences in the energy consumption levels between the three modes of occupancy. Lastly, hourly profiles were analyzed to estimate occupant presence and schedules during typical work and nonwork days. Highlights of the results show that building commissioning reduced total energy consumption by 15%, while reduced occupancy led to a 10% decrease. Low sensitivity to outdoor conditions (e.g., irradiance and outdoor temperature) on energy consumption was also observed. Future research can consider investigating commissioning projects’ energy savings from other Canadian offices with similar design goals (e.g., net-zero energy) and uncovering a relationship between occupancy (i.e., uncovered through occupant sensor data) and energy consumption.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".