AI-Enhanced Power Management System for Buildings: A Review and Suggestions
Why this work is in the frame
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Bibliographic record
Abstract
Modern power management systems are highly recommended for institutes to enhance power saving, as they effectively stratify their activities.These systems are essential to integrate intelligent methods, such as machine learning and deep learning, to make optimal decisions in managing consumed power and significantly minimize energy usage.In this review, we delve into the concept of smart energy management, focusing on three key areas: Wireless Sensor Networks (WSN), Building Information Modeling (BIM), and Artificial Intelligence (AI) techniques represented by deep learning (DL) and machine learning (ML) approaches.The primary objective of this review is to propose an optimized model for an energy management system based on a clustered WSN that collects the required information.Additionally, we explore how data from buildings' BIM systems can be effectively utilized to create an optimized method for managing power consumption using ML/DL techniques, specifically applicable to smart buildings.Implementing this solution can efficiently manage power consumption in institute buildings, leading to significant energy savings and reduced related costs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it