EV Charging Utilization Forecasting With Synthetic Time-Series Data
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Solution Overview
Problem
Existing solutions for long-term forecasting of electric vehicle (EV) charging station utilization are inadequate due to a scarcity of historical data, uneven distribution of existing locations, and the influence of factors like policy changes and economic fluctuations, which complicates the prediction of future EV adoption.
Innovation Solution
A system utilizing a Time-series Generative Adversarial Network (TimeGAN) model with custom enhancements generates synthetic data to augment limited observations, integrating heterogeneous data sources and ensuring strategic feature importance, enabling robust long-term forecasting for EV charging station allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If long-term forecast models are used to predict EV charging utilization, then future utilization can be estimated, but the models are not applicable due to limitation of historical data
Solution Approach 1:
The system performs preliminary actions by collecting and preprocessing heterogeneous data sources (EV sales, registrations, demographics, traffic, retail sales) before the actual forecasting task. This includes data cleaning, feature engineering, and creating synthetic training data that simulates future scenarios, enabling the model to be prepared in advance for long-term predictions despite limited historical charging utilization data
Solution Approach 2:
The system creates synthetic copies of historical data through data augmentation techniques and generative models. By synthesizing additional training samples that mimic real-world patterns, the model gains sufficient training material to learn long-term trends without requiring extensive actual historical charging data, thus resolving the data scarcity issue
2Reliability
If existing charging station locations are used for forecasting, then current utilization patterns can be analyzed, but the distribution is uneven and does not represent future adoption levels
Solution Approach 1:
The system applies local quality by analyzing forecasting requirements at specific geographic locations with different characteristics. Instead of using a uniform approach, it tailors data collection and model parameters to local conditions (urban vs. rural, different states, specific retail locations), ensuring that forecasts are representative of each location's future potential rather than being skewed by current uneven distribution
Solution Approach 2:
The system transitions from analyzing only existing charging station locations to incorporating multiple dimensions including EV sales data, registration trends, demographic factors, traffic patterns, and retail sales data. This multi-dimensional approach allows forecasting for locations without current charging infrastructure by projecting future adoption based on these additional dimensions
3Ease of operation
If short-term utilization forecasts are used, then day-to-day operations can be planned, but they do not apply to long-term location selection strategy
Solution Approach 1:
The system implements dynamics by creating a flexible forecasting framework that can adapt the time horizon based on the specific application. The model structure allows switching between short-term operational forecasting (days to weeks) and long-term strategic forecasting (years) by adjusting input data ranges and model parameters, making it versatile for both operational planning and location selection strategy
Data Source
AI summary
Systems and methods for forecasting energy utilization of charging stations to determine charging station allocation are disclosed. In some embodiments, a disclosed method includes: receiving a forecast request seeking utilization of electric vehicle (EV) charging stations at a location in a future time period; determining at least one EV related feature based on the forecast request; computing at least one forecasted feature value for the at least one EV related feature associated with the location in the future time period; generating, using a utilization model, forecasted utilization data based on the at least one forecasted feature value; and transmitting the forecasted utilization data to a computing device.