A Novel Method to Assess Long-Term Building Energy Consumption Variation through Weather Normalization and Multiple Year Building Energy Simulation
Bibliographic record
Abstract
Building energy performance is subject to influences such as weather variations, changes in the building conditions, as well as changes in occupant behaviour and operational schemes. This research builds on and improves the efficacy of the Degree-days Ratio-based method, a commonly used weather normalization method, by way of introducing a weighting exponent and excluding the consumption values during the “Shoulder Season” months in the dataset for deriving an indicator of the changes in building condition or system operations over years. These two proposed schemes were tested using both simulated and measured space heating and cooling energy consumption data. Prototype commercial building models were simulated with published Typical Year (CWEC2016) and Historical Year Weather Data (CWEEDS 1998-2014). Based on standard deviation and CVRMSE figures between the normalized values and the reference value simulated using Typical Year weather data, the proposed method resulted in less year-to-year energy consumption variations when compared to two conventional methods. The measured energy consumption data of an in-service Case Study Building were weather-normalized to derive an energy consumption variation trend. Normalizing with the proposed method yielded a more definitive energy consumption variation trend which is indicative of the long-term energy performance due to changes in building condition and operational schemes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".