MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization
Bibliographic record
Abstract
A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO’s effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4–7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".