Green IoT: AI-Powered Solutions for Sustainable Energy Management in Smart Devices
Bibliographic record
Abstract
In response to global social and environmental challenges, cities worldwide increasingly adopt sustainable infrastructure strategies. This paper presents the architecture and results of implementing IoT-based Smart Green Energy (IoT-SGE) solutions to enhance energy management in urban settings. Key strategies include sustainable mobility policies, energy-efficient building updates, renewable energy production, improved waste management, and ICT integration. A key focus is on the development of smart city energy systems through mixes of on-site and off-site energy sources, where IoT technologies have a key role in monitoring and control. In this paper, it is proposed a technique that utilizes IoT sensors and deep reinforcement learning to predict energy demand and optimize consumption. This comprises various aspects of the architecture, including IoT devices for data collection, machine learning algorithms for predictive analytics, and best practices in management for sustainable energy. Testing results are presented, showing that the IoT-SGE solutions significantly improve energy efficiency and sustainability. In particular, the performance of this synthetic dataset using an even-thoroughly-tuned XGBoost model was moderate, with a Mean Squared Error of 9028.58 and R² of 0.22.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".