Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings
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
This research utilizes swarm intelligence algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and hybrid PSO-ACO-to optimize energy efficiency and thermal comfort in smart building HVAC systems. A thorough experimental analysis was done in a 500-kW cooling capacity smart building in 20 monitored temperature zones for 12 months. The hybrid PSO-ACO model performed the best energy savings of 28.9%, better than PSO (23.7%) and ACO (20.5%), and also saving 29.2% peak load demand. Thermal comfort analysis through Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) metrics showed better indoor conditions, with the hybrid model keeping room temperatures within ±0.8°C of the setpoint and bringing the PPD index down to 8.5%. Statistical validation through ANOVA and t-tests supported the energy and comfort gains with p-values always less than 0.05. The hybrid model also exhibited faster convergence, completing the optimization in 120 iterations, 20% faster than ACO. Economic analysis estimated annual cost savings of $12,000 for a 10,000 m 2 building with a return on investment within 2.5 years, while saving 35.2 metric tons of CO 2 emissions per year. The results illustrate the outstanding performance of hybrid swarm intelligence algorithms to improve HVAC efficiency and occupant comfort, a scalable and inexpensive solution for smart building management. Future research will utilize machine learning combined with swarm intelligence for adaptive and predictive HVAC control.
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 | 0.000 | 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 it