Replenishment Station Location Prediction Using Demand Aggregation
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Solution Overview
Problem
The placement of electric vehicle charging stations is often ad hoc, leading to improper utilization and inadequate infrastructure to meet the needs of electric vehicles, as the technology is still developing and infrastructure is sparse.
Innovation Solution
A method and system that utilize historical demand data, point of interest locations, and traffic information to create prediction models for identifying optimal locations for replenishment stations, using processors to aggregate demand predictions and select locations based on a defined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electric vehicle charging stations are placed on an ad hoc basis, then the infrastructure can be quickly established, but the utilization efficiency deteriorates and locations are not suited for proper infrastructure
Solution Approach 1:
The system performs preliminary analysis of historical demand data, point of interest locations, and traffic information before placing charging stations. Multiple prediction models are created and executed in advance to identify optimal locations, ensuring that stations are placed at sites with high predicted demand before infrastructure deployment occurs.
Solution Approach 2:
The system uses historical demand data from existing replenishment stations as feedback to continuously improve location predictions. The prediction models are trained on past performance data, and the system aggregates predictions from multiple models to refine location recommendations, creating a feedback loop that improves utilization efficiency.
2Measurement precision
If multiple prediction models are created and aggregated, then the location prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The system segments the location prediction task into three distinct prediction models, each focusing on different aspects: historical demand patterns, point of interest influence, and traffic information. By dividing the complex prediction problem into manageable segments, the system achieves higher overall accuracy while maintaining manageable complexity through modular model design.
Solution Approach 2:
The system merges the outputs of multiple prediction models by aggregating their predictions. This combination approach consolidates the strengths of individual models to produce a more accurate overall prediction, while the aggregation process is designed to be computationally efficient, balancing accuracy improvements with system complexity management.
Data Source
AI summary
A method and a system are provided for identifying one or more locations for placement of one or more replenishment stations for one or more vehicles. The method comprises receiving a historical demand data at a plurality of existing replenishment stations within a pre-defined area. The method identifies one or more point of interest locations within the pre-defined area based on a map data. Further, the method receives traffic information between a plurality of road intersections within the pre-defined area. Based on an aggregation of a first demand prediction, a second demand prediction, and a third demand prediction, the method predicts a replenishment demand at a plurality of locations. The method further identifies the one or more locations from the plurality of locations for placement of the one or more replenishment stations based on the predicted replenishment demand at the plurality of locations and a pre-defined threshold.