The application of Differential Evolution to HVAC optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examined the optimum window area, building aspect ratio and building orientation for an apartment building in two different locations: Winnipeg, Canada and Miami Florida. This application was based on a Python script program called the EnergyPlus analysis program and utilized Differential Evolution as the numerical optimization scheme. EnergyPlus is a whole building load and energy analysis program that is well understood and freely available on the internet. It can be used to both size equipment and perform annual energy analysis. It is widely used in the HVAC industry and has demonstrated a good track record. Differential Evolution is a genetic algorithm that is used to numerically find the global optimum of problems that can have continuous, integer, and discreet variables. It uses the existing population in a generation to determine mutation, and is purported to be faster than other genetic algorithms. The annual energy cost was used for the cost function of the 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it