Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis

TL;DR

This paper introduces a multivariate LSTM with attention and explainable AI techniques to accurately forecast EV charging demand at 15-minute intervals, aiding infrastructure planning and management.

Contribution

It presents a novel combination of multivariate LSTM, attention mechanisms, and SHAP for explainability in EV demand forecasting, improving prediction interpretability.

Findings

High-accuracy demand forecasts demonstrated in simulations.

Explainability via SHAP reveals key demand-influencing factors.

Framework supports better infrastructure planning.

Abstract

As the number of electric vehicles (EVs) continues to grow, the demand for charging stations is also increasing, leading to challenges such as long wait times and insufficient infrastructure. High-precision forecasting of EV charging demand is crucial for efficient station management, to address some of these challenges. This paper presents an approach to predict the charging demand at 15-minute intervals for the day ahead using a multivariate long short-term memory (LSTM) network with an attention mechanism. Additionally, the model leverages explainable AI techniques to evaluate the influence of various factors on the predictions, including weather conditions, day of the week, month, and any holiday. SHapley Additive exPlanations (SHAP) are used to quantify the contribution of each feature to the final forecast, providing deeper insights into how these factors affect prediction accuracy. As a result, the framework offers enhanced decision-making for infrastructure planning. The efficacy of the proposed method is demonstrated by simulations using the test data collected from the EV charging stations at California State University, Long Beach.