Towards the Enhancement of Buildings’ Sustainability: IoT-Based Building Management Systems (IoT-BMS)
Bibliographic record
Abstract
Abstract The building sector is the primary consumer of energy, especially electricity. Energy consumption results in greenhouse gas emissions, depletion of natural resources, and finance consumption. Nowadays, buildings are increasingly expected to meet higher and more complex performance requirements. Among these requirements, energy efficiency is recognized as an international goal to promote energy sustainability. Therefore, monitoring, controlling, and managing energy are the key goals of building management that opt for energy efficiency and cost-effective operation and maintenance, which are the main objectives of sustainable development goals. The building sector is significant in its function and requires more energy to operate and maintain, especially for lighting, achieving appropriate thermal comfort, and managing IT systems and other equipment. The reliability and flexibility offered by wireless technologies have been the driving force toward the vision of the Internet of Things (IoT). They have contributed to attracting growing interest in the market. This work presents an energy-efficient IoT solution to monitor the energy consumption model by deploying a Building Management System (BMS). Integrating multiple battery-operated sensors into the building allows critical data to be dynamically provided in real-time to improve overall building efficiency. Introducing the IoT in managing energy in buildings can be more cost-effective and convenient than traditional building BMSs.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
IoT-based building management system for energy efficiency; an engineering solution.
This work presents an IoT building-management solution and does not study research itself.
IoT building-management systems for energy efficiency; applied engineering, not a study of research.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".