Power Control Device Accuracy Visualization

The patent implements feedback by calculating the difference between predicted power values and actual measured power values, then using this difference information to adjust and improve future predictions. The system continuously monitors prediction accuracy and feeds this information back to refine the prediction model, thereby improving reliability while maintaining productivity.

The patent changes parameters by introducing accuracy indicators (such as standard deviation or confidence intervals) to the predicted power values. Instead of providing single-point predictions, the system provides prediction ranges or accuracy metrics that allow users to assess reliability. This parameter enhancement resolves the contradiction by making both productivity and reliability visible and usable simultaneously.

The patent applies self-service by enabling the prediction system to automatically calculate its own accuracy metrics and adjust its parameters without external intervention. The system autonomously computes correlation coefficients, determines optimal prediction models, and refines its own performance, thereby improving precision without proportionally increasing operational complexity.

The patent uses copying by creating simplified models that replicate the behavior of complex prediction algorithms. Instead of directly implementing sophisticated machine learning models, the system uses correlation-based approaches that copy the essential predictive behavior with reduced computational complexity, maintaining precision while lowering system complexity.