Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility

Abstract

Electromobility (e-mobility) reduces global fossil fuel-based energy demand and promotes the usage of renewable energy sources. Different types of electric vehicle (EVs) production have increased recently due to their need. However, there is no fixed charging behavior, and demand exists. Uncontrolled EV charging may lead to a harmful impact on the power grid. Besides, most renewable energy sources depend highly on the weather and location. Thus, analyzing, forecasting, and adapting EV charging demand is essential to prevent power outages. Deep Learning (DL) models are a de facto standard technology for energy forecasting in many scenarios. Still, there is a lack of real-world data in the e-mobility domain to train a DL model from scratch. Besides, DL models have explainability issues, which can hinder decision-making. This article proposes a novel framework for addressing data scarcity issues, enabling continuous adaptation to change, and improving explainability.