VoltaResBot: A Machine Learning Model for Optimal Energy Management in Multi-Component Robotic Systems Integrated with Photovoltaics, and Storages
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
Predictive energy management models considerably advance financial and environmental analyses of industrial robotic manipulator energy consumption. As industries increasingly prioritize sustainability and cost-effectiveness, optimizing energy utilization becomes crucial. Consequently, this paper presents VoltaResBot, a machine learning-driven predictive techno-economic model for optimal energy management in multi-component robotic systems integrated with photovoltaics (PVs), electricity grid, and storage (ESS). VoltaResBot utilizes Support Vector Machines (SVM) to predict and optimize energy consumption, facilitating precise control of a 6 Degrees of Freedom (6 DoF) robotic manipulator. The findings of VoltaResBot indicate its effectiveness in achieving optimal energy utilization, significantly reducing environmental. Key Performance Indicators (KPIs) employed in the evaluation include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared ($\mathbf{R}^{\mathbf{2}}$), presenting the model’s accuracy. Comparative analyses with traditional machine learning models further demonstrate the superior performance of VoltaResBot.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it