Chiller Performance Evaluation and Optimization Algorithms for Existing Buildings
Bibliographic record
Abstract
There is a growing need for energy efficient air-conditioning systems as climate change increases cooling loads and is accelerated by resultant GHG emissions. Residential buildings are a prime candidate for Retro-Commissioning (RCx) as their control algorithms are not often reviewed or revised post construction and commissioning. A data-driven approach is implemented to create regression models predicting chiller power consumption and building thermal response using readily available data in residential Building Automation Systems ("BAS") and weather data. Best case models predict chiller power draw within RMSE 3.1 kW o (0.52% of full load) over the cooling season and building thermal response of RMSE 0.71 C. This research also investigated wrapping models into a model predictive control ("MPC") algorithm using Bayesian Optimization to update chiller setpoint parameters in real-time to reduce chiller power consumption. MPC algorithms deployment is simulated using data logged from two typical multi-unit residential buildings in ASHRAE Climate Zone 5. Best case simulated optimizer performance showed potential chiller energy savings of approximately 12%.
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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".