VoltaResBot: A Machine Learning Model for Optimal Energy Management in Multi-Component Robotic Systems Integrated with Photovoltaics, and Storages
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".