Internet of Things Structure for Intelligent Energy in Structures: Concepts, Model, and Tests
Bibliographic record
Abstract
The Internet of Things (IoT) has the potential to totally transform the way in which buildings manage energy, and this essay examines how this promise may be realised. In the paper, a new framework called the "Internet of Things Structure for Intelligent Energy" is presented. This framework is intended to increase energy efficiency and sustainability in built environments. The article provides a description of the design of this framework, as well as an investigation into the incorporation of the Internet of Things (IoT) in energy management and a review of relevant literature to give a theoretical foundation. Following the successful completion of an experimental setup for practical application, an iterative optimisation technique and a model design that is clearly described are carried out. There is a comprehensive analysis of performance measurements that is carried out, as well as a comparison to other well-established methods of energy management. The findings of the research, which are backed up by insights that are driven by data, demonstrate considerable improvements in energy efficiency and adaptability to shifting conditions. The comprehensiveness of this Internet of Things-enabled energy management system is further shown by the integration of user experience and environmental impact studies.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".