Machine Learning Power Prediction for Charging Devices
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Solution Overview
Problem
Conventional technologies fail to accurately predict the amount of electric power that can be supplied by charging devices during demand response periods due to variability based on the number of batteries and charging capacity, making it difficult to determine if sufficient power will be available.
Innovation Solution
A power prediction system using machine learning to forecast the amount of electric power that can be supplied by a charging device, incorporating usage information such as battery number, charging capacity, weather, and location-specific data to create region-specific models for accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods are used to forecast power supply from charging devices, then the prediction process is simple, but the prediction accuracy is insufficient due to variability in battery number and charging capacity
Solution Approach 1:
The patent replaces conventional simple prediction methods with a machine learning-based prediction system. The prediction device uses trained models that process multiple input features (historical power supply data, battery information, charging capacity, environmental factors) to generate accurate predictions of future power supply from charging devices during demand response periods, thereby resolving the contradiction between prediction accuracy and system complexity.
2Reliability
If the charging device supplies power during demand response periods, then it can meet power supply/demand tightness requirements, but the amount of power available varies with battery number and charging capacity making reliable supply difficult
Solution Approach 1:
The prediction system incorporates feedback mechanisms by continuously processing actual power supply data from charging devices along with battery information, charging capacity, and environmental factors. This feedback loop enables the system to adapt to varying power availability while maintaining reliable predictions, resolving the contradiction between power supply reliability and flexibility.
3Measurement precision
If machine learning models are trained with detailed usage information, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models with comprehensive usage information including historical power supply data, battery characteristics, charging capacity, and environmental factors before deployment. This pre-processing and training phase consolidates the data processing complexity into an initial setup, allowing the model to deliver accurate predictions without real-time processing burden, thus resolving the contradiction between prediction accuracy and data processing complexity.
Data Source
AI summary
A power prediction system includes a battery removably mounted on an electric power device using electric power, a charging device configured to charge the battery, and a power prediction device configured to predict an amount of electric power capable of being supplied by the charging device to outside of the charging device through machine learning on the basis of usage information indicating at least one of the usage state and the usage environment of the charging device.