Adaptive Forecast Curve Approximation in Technical System Control
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Solution Overview
Problem
Existing control devices for technical systems struggle to provide adaptable and accurate control of controllable components based on forecast curves, as they rely on predetermined and equidistant supporting points, leading to limited accuracy and flexibility.
Innovation Solution
The introduction of an optimizer with an analysis unit and an approximator that sets supporting points based on the analyzed values of the forecast curve, allowing for a more flexible and accurate approximation of the forecast curve.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predetermined equidistant supporting points are used to approximate the forecast curve, then the device complexity is reduced and the control process is simplified, but the measurement precision and forecast accuracy deteriorate
Solution Approach 1:
The patent changes the parameter of supporting point selection from fixed equidistant intervals to dynamically determined positions based on forecast curve analysis. The analysis unit identifies characteristic points (minima, maxima, turning points) and sets supporting points at these locations, transforming the approximation method from static to adaptive, thereby improving accuracy without proportionally increasing complexity
Solution Approach 2:
The patent introduces dynamics into the supporting point selection process by making the positions of supporting points adaptable based on the forecast curve characteristics. The analysis unit continuously evaluates the forecast curve and adjusts supporting point locations accordingly, allowing the system to respond to changing forecast patterns while maintaining manageable complexity through automated analysis
2Measurement precision
If the number of supporting points is increased to improve forecast accuracy, then the measurement precision improves, but the computing effort and device complexity increase
Solution Approach 1:
The patent optimizes the parameter of supporting point quantity by selecting points based on forecast curve characteristics rather than using a fixed large number. The analysis unit identifies only the necessary characteristic points (minima, maxima, turning points), reducing the number of supporting points needed compared to uniform discretization while maintaining or improving approximation accuracy
Solution Approach 2:
The analysis unit performs preliminary analysis of the forecast curve to identify characteristic points before setting supporting points. This preliminary action allows the system to determine the optimal number and position of supporting points in advance, avoiding the need for excessive points and reducing subsequent computing effort in the approximation process
3Ease of operation
If fixed equidistant time intervals are used for supporting points, then the control process is simplified and device complexity is reduced, but the adaptability to dynamically changing forecast curves deteriorates
Solution Approach 1:
The patent transforms the static equidistant time interval approach into a dynamic system where supporting point positions adapt to forecast curve characteristics. The analysis unit evaluates the forecast curve and determines supporting point locations based on actual curve features, enabling the system to adapt to varying forecast patterns while maintaining operational simplicity through automated analysis
Solution Approach 2:
The patent changes the parameter of time interval between supporting points from fixed equidistant values to variable intervals determined by forecast curve characteristics. Supporting points are placed at characteristic points regardless of their temporal distance, allowing the system to adapt to dynamic forecast changes while keeping the control process simple through rule-based analysis
Data Source
AI summary
A control device includes: a receiving unit, the control device being configured for controlling a technical system including at least one controllable component with respect to a target variable taking into consideration a forecast curve, which is provided with a plurality of values for a plurality of points in time in a future time period by the receiving unit; and an optimizer including an analysis unit configured for analyzing the forecast curve and an approximator configured for specifying an approximated forecast curve, the analysis unit being configured for setting a plurality of supporting points based on analyzed ones of the plurality of values of the forecast curve, and the approximator being configured for specifying the approximated forecast curve with the plurality of supporting points that are set based on the analyzed ones of the plurality of values of the forecast curve.