Deep Adaptive Knowledge-Aware Learning for E-Mobility
M. Ali. Organic Computing - Doctoral Dissertation Colloquium 2022, kassel university press, (2022)
Electromobility can reduce global energy demand by reducing
the dependence on fossil fuels. Many different types of electric vehicles
(EVs) have been produced recently and EV usage is also rising.
However, there is no fixed charging behavior of these EV users. Moreover,
most renewable energy sources are stochastic in nature. Thus, it is
essential that EV charging demand is controlled and adapted in advance
to prevent catastrophic failures such as blackouts. Deep learning models
are standard technology for power forecasting in many scenarios. However,
there is a lack of real-world heterogeneous multi-source data in the
e-mobility domain to train a deep learning model from scratch. Moreover,
deep learning (DL) models lack model explainability. Therefore,
EV stakeholders can benefit from a framework that coordinates deep
transfer learning, interactive machine learning, and heterogeneous graph
representation learning (to ensure self-explainability). This work aims to
develop a novel framework to solve e-mobility-related tasks such as forecasting
EV loads efficiently in a resource-constrained environment (data,
computing power, etc.). It also describe various research challenges and
potential approaches to build the proposed framework. This work also
present a deep generative model to synthesize EV charging session data.
