Rapid Workflow for Energy Model Calibration, Retrofit Optimization, and Uncertainty Analysis of Large Building Energy Retrofits
Bibliographic record
Abstract
The building sector has been targeted to have net zero emissions by 2050.Considering the low rate of new construction and degradation of the building's materials properties over time, the retrofit of existing buildings can play a crucial role in achieving these goals.Moreover, determining a cost-effective strategy for reducing GHG emissions is necessary.Due to budget and time limitations, determining the optimal retrofit and operating strategy to minimize GHG emissions, energy usage, and the life-cycle cost is vital for building decarbonizing.This research aims to develop a rapid framework for creating a retrofit roadmap using multi-objective optimization of building energy retrofits.The proposed thesis is divided into four main parts to achieve these objectives.First, an optimization algorithm calibrates the energy model using various measured data resolutions.The metered data of a large office building in Ottawa, Canada, are used as a case study.The second part develops a rapid framework for multi-objective optimization of building retrofit considering life cycle cost and GHG emissions.The proposed workflow is applied to an office building's retrofit analysis in Ottawa, Canada.Then, a methodology is developed to create a roadmap for building energy retrofitting based on the uncertainty analysis results.In this step, the uncertainty of the optimal strategy due to the fluctuation of some economic rates is evaluated using the Monte Carlo simulation.Finally, a hybrid model is proposed for retrofit optimization.This approach integrates physical modelling (EnergyPlus) with advanced data-driven approaches (machine learning) to optimize retrofit strategies.Utilizing machine learning techniques, particularly the Random Forest model, significantly improves accurate predictions and efficient retrofit optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".