Sustainable Energy Management System for AIoT Solutions Using Multivariate and Multi-Step Battery State of Charge Forecasting
Bibliographic record
Abstract
The convergence of Artificial Intelligence (AI) with Internet of Things (IoT) technologies, often referred to as AIoT, is transforming aspects of modern life, such as smart cities. This transformation, however, brings with it challenges, including energy management. In addressing this issue while upholding responsible AI principles, it is important to prioritize the sustainability of AIoT solutions by a promising approach which is using renewable energy sources. While renewable energy offers numerous advantages, its intermittent nature necessitates effective power management systems. Developing a power management system serving as a decision-making platform for AIoT-driven solutions is the goal of this study. This platform contains two critical components: accurate forecasts of battery "State of Charge" (SoC), and the implementation of appropriate control strategies. These strategies include adjusting energy consumption patterns to ensure stable and reliable system operation. This study focuses on accurate battery SoC forecasting, to this end, an experiment has been designed, and a data logging system has been developed to produce suitable data since publicly available datasets do not align with the specific characteristics and requirements of the research. The SoC forecasting in this study has been addressed as a multivariate and multi-step time series forecasting problem, where various machine learning and deep learning models including Decision Tree (DT), Random Forest (RF), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU) were benchmarked. Extensive evaluations have been conducted for different forecasting horizons on datasets with varying time intervals. It is concluded that the Bi-GRU model outperformed other models across datasets with varying time intervals and forecast horizons according to Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) evaluation metrics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".