EV Charging Point Availability Prediction Using Clustered Forecasting
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Solution Overview
Problem
Conventional methods for forecasting electric vehicle charging point (EVCP) availability are complex, resource-intensive, and often lead to suboptimal predictions, resulting in underutilization or congestion due to the oversight of crucial factors.
Innovation Solution
An apparatus and method utilizing machine learning models to cluster EVCPs based on specific features, train a second model for accurate availability prediction, and update clusters based on confidence scores to optimize forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional forecasting methods are used for EVCP availability, then predictions can be generated, but the computational resources required are excessive and the accuracy is suboptimal
Solution Approach 1:
The patent segments the EVCP dataset into multiple clusters based on similarity in availability patterns and charging behaviors. By dividing the data into smaller, more homogeneous groups, the system can apply forecasting models more efficiently to each cluster rather than processing all data uniformly, reducing overall computational resources while improving prediction accuracy through targeted analysis.
Solution Approach 2:
The patent transforms the forecasting approach by changing key parameters: instead of using raw individual EVCP data points, it aggregates data at the cluster level and uses cluster-level statistics as forecasting inputs. This parameter transformation reduces data dimensionality and computational complexity while capturing essential patterns through the clustering structure.
2Reliability
If conventional forecasting methods are used for EVCP availability, then predictions can be generated, but the system complexity and resource requirements become impractical for large-scale deployment
Solution Approach 1:
By segmenting EVCPs into clusters with similar behaviors, the system reduces the complexity of managing individual EVCP forecasts. Each cluster can be managed as a unified entity with shared forecasting models, significantly reducing the number of models needed and simplifying system architecture while maintaining or improving reliability through the capture of common patterns.
Solution Approach 2:
The patent merges individual EVCP forecasting tasks into cluster-level forecasting. By combining multiple EVCPs into clusters and applying a single forecasting model per cluster, the system reduces the total number of models and computations required, making large-scale deployment practical while maintaining forecasting reliability through the aggregated insights.
3Measurement precision
If conventional forecasting methods are used for EVCP availability, then predictions can be generated, but crucial factors are overlooked leading to suboptimal predictions and inefficient resource management
Solution Approach 1:
The patent applies local quality by creating clusters with homogeneous characteristics, allowing the system to capture location-specific and behavior-specific patterns that conventional methods overlook. Each cluster represents a local group of EVCPs with similar availability patterns, enabling the forecasting model to account for local factors such as geographic location, charging demand patterns, and operational characteristics that would be lost in aggregate analysis.
Data Source
AI summary
An apparatus and method for predicting availability data of electric vehicle charging points (EVCPs) are provided. The apparatus receives EVCP data associated with each of a plurality of EVCPs. The apparatus determines clustering features for each of the plurality of EVCPs based on the EVCP data. The clustering features comprise a duration parameter, a predefined charging status parameter, a charging gap parameter, or a combination thereof. The apparatus generates clusters using a first model based on the clustering features. Each of the clusters comprises at least one of the plurality of EVCPs. The apparatus trains a second model to predict availability data associated with each EVCP within each of the clusters based on a data point associated with one or more EVCPs within said cluster.