A hybrid attention‐based long short‐term memory fast model for thermal regulation of smart residential 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
Abstract An attention‐based long short‐term memory (ALSTM)‐fast model predictive control (MPC) thermal regulation system for buildings is presented. The proposed system is developed to address the challenges associated with traditional heating, ventilation, and cooling (HVAC) control systems, often designed with fixed setpoints and static control strategies, leading to poor performance and suboptimal energy efficiency. The ALSTM‐Fast MPC system, on the other hand, performs the integration of deep learning and optimisation algorithms to predict the thermal behaviour of buildings and optimise the HVAC system control for thermal comfort and energy efficiency. The ALSTM‐Fast MPC system was implemented and evaluated on a real‐world data collected from a building automation system. Additionally, extensive experiments were conducted to analyse the system's performance. The results demonstrated the system's adaptability to changing thermal dynamics and occupancy patterns and its ability to achieve robust and efficient thermal regulation. As a result, a solution for optimising HVAC control in buildings is provided by the proposed ALSTM‐Fast MPC system.
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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