An Improved Temperature Prediction Technique for HVAC Units Using Intelligent Algorithms
Bibliographic record
Abstract
This paper proposed a methodology to automatically search for the best combination of the Learning Horizon (LH) and Predicting Horizon (PH) with the objective of improving the prediction performance for a temperature prediction technique for HVAC units. Three intelligent algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Greedy Algorithm (GRA), are integrated into a temperature prediction process for identifying the parameters of a thermodynamic model. The developed temperature prediction technique was tested, validated, and evaluated in a case study. This case study also compared the prediction performances of the three different optimization algorithms mentioned earlier and explored the impact of the LHs and PHs on the prediction performance. By setting up the selection standards for evaluating the prediction performances of the temperature prediction technique, the most suitable algorithm was then selected along with the best combination of the LH and PH.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".