AI-Enhanced Power Management System for Buildings: A Review and Suggestions
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".