Harnessing the Synergy of Artificial Intelligence and Blockchain Technology in Smart Buildings for Enhanced Efficiency and Security
Bibliographic record
Abstract
With the development of smart buildings as a significant part of green cities, the introduction of Artificial Intelligence (AI) and Blockchain technologies suggests a potential breakthrough in operational efficiency, energy efficiency, and security. This paper explores the synergistic effect of AI and Blockchain on smart buildings, examining the potential of combining AI and Blockchain to achieve optimized building systems and enhanced data security. The main goals of this study are to discuss the integration of AI and Blockchain technologies in the context of modern building efficiency, security, and sustainability, as well as the challenges encountered by building management systems in the past. The narrative review methodology was employed, and a range of case studies, literature, and practices were examined as part of the analysis. The most outstanding insights include the fact that AI can help optimize existing operational systems positively, HVAC, lighting, and predictive maintenance. In contrast, Blockchain can guarantee safe and transparent management of information, immutability, and decentralization. A combination of these technologies contributes to improved energy efficiency, lower costs, and enhanced security. The paper concludes with a note that the full potential of AI and Blockchain in creating more innovative and more sustainable buildings can be achieved by focusing on scalability, integration complexity, and interoperability.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".