Enhancing Thermal Comfort and Energy Efficiency in Buildings Using Artificial Intelligence: A Systematic Literature Review
Bibliographic record
Abstract
Improving thermal comfort in buildings is essential for enhancing occupant satisfaction and well being, but it can often lead to higher energy consumption. Adjusting heating, ventilation, and air conditioning (HVAC) systems, optimizing airflow, or maintaining consistent temperatures can increase energy use. Recent advancements in Artificial Intelligence (AI) offer the ability to manage both thermal comfort and energy efficiency simultaneously without sacrificing one for the other. This study utilizes a Systematic Literature Review (SLR) of 230 studies to explore AI's potential in improving thermal comfort and energy efficiency. The findings highlight six areas where AI outperforms traditional methods: (1) thermal comfort prediction, (2) personalized thermal comfort models, (3) occupancy detection and behavior prediction, (4) building design for comfort and efficiency, (5) fault detection and system diagnostics, and (6) occupant health and integration with other indoor environmental quality (IEQ) factors. This study highlights several examples of AI's potential and suggests future research directions to fully harness these opportunities.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".