Charging Station Congestion Prediction Using Variance-Based Weight Correction
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Solution Overview
Problem
Current systems for predicting congestion at charging stations lack reliability due to the inability to accurately account for the variance in the number of waiting vehicles, leading to inconveniences for users and inefficient distribution of charging station demand.
Innovation Solution
A system and method that utilize an artificial intelligence model to predict the number of waiting vehicles, with a prediction model unit, a change calculation unit, and a predicted value correction unit, which calculates variance and applies weights based on congestion levels to improve the accuracy of congestion predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an artificial intelligence model is used to predict the number of waiting vehicles, then the prediction capability is improved, but the reliability of the predicted value deteriorates due to inability to account for variance in waiting vehicles
Solution Approach 1:
The system implements feedback by calculating the variance between predicted values and actual values, then using this variance information to dynamically adjust weights in subsequent predictions. The change calculation unit computes variance based on historical data, and the predicted value correction unit applies these variance-based weights to improve future prediction reliability, creating a closed-loop feedback mechanism.
Solution Approach 2:
The system changes parameters by dynamically adjusting weights based on variance calculations. Instead of using fixed weights for predicted values, the system modifies the weight parameters according to the calculated variance, allowing the prediction model to adapt to changing conditions and improve reliability while maintaining prediction capability.
2Reliability
If the system calculates variance and applies dynamic weights to correct predicted values, then the reliability of predictions is improved, but the device complexity increases
Solution Approach 1:
The system segments the prediction process into distinct functional units: a prediction model unit for initial predictions, a change calculation unit for variance computation, and a predicted value correction unit for applying weights. This segmentation allows each unit to perform a specific function, improving reliability through specialized processing while managing complexity through modular design.
Solution Approach 2:
The variance calculation acts as an intermediary between the initial AI prediction and the final corrected value. Instead of directly modifying the AI model, the system introduces a variance-based weight calculation as an intermediate step that bridges the predicted value and actual conditions, improving reliability without fundamentally changing the underlying AI model.
3Adaptability or versatility
If the system uses variance-based weight adjustment to reflect changing conditions, then the adaptability to dynamic environments is improved, but the calculation time and processing requirements increase
Solution Approach 1:
The system applies partial action by calculating variance only for key parameters and applying weights selectively to predicted values. Instead of reprocessing all data, the system focuses on calculating the essential variance metric and applying the corresponding weight adjustment, achieving adaptability to changing conditions while minimizing additional calculation time.
Data Source
AI summary
The present disclosure relates to a system and a method for predicting the level of congestion at a charging station. The system for predicting the level of congestion at the charging station may include the following units: a prediction model unit, a change calculation unit, and a predicted value correction unit.