Are complex baseline energy use models needed to accurately predict retrofit savings?
Bibliographic record
Abstract
Abstract Baseline energy use models play a pivotal role in estimating energy savings after a building undergoes a retrofit. While industry guidelines prescribe goodness-of-fit thresholds, it remains unclear if compliance to these thresholds guarantee accurate estimation of retrofit savings. To this end, a simulation model of a one-storey commercial building is developed in Energy Plus before and after a major envelope retrofit. Simulations are executed using actual meteorological year data of two different years, representing the pre-retrofit and post-retrofit years. Seven different regression models of varying complexity are trained with subhourly preretrofit data to estimate the impact of retrofit savings. The results indicate that models which accurately fit the pre-retrofit (base year) data maintained this accuracy in the assessment (postretrofit) as well. Expectedly, increasing model complexity translated to lower CV(RMSE) and NMBE. However, there was no clear relationship between the goodness-of-fit metrics for the base year and the ability to accurately predict annual retrofit savings. The simplest model tested (a three-parameter change point model) was able to make accurate predictions of overall annual heating and cooling savings despite having a fairly large CV(RMSE) and not being able to capture hourly variations in heating and cooling loads.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".