Green IoT: AI-Powered Solutions for Sustainable Energy Management in Smart Devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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 it