M. Sc. Mohammad Wazed Ali

AI4CIS (AI for Computationally Intelligent Systems)

Fax: +49 561 804-6022
Email: wazed.ali[at]uni-kassel[dot]de

Publications

[ 2022 ]

2022[ to top ]

Ali, M.W.: Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility In: IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). bll 57–60. IEEE (2022). https://doi.org/10.1109/ACSOSC56246.2022.00030.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
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.
@inproceedings{ali2022heterogeneous, 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.}, author = {Ali, Mohammad Wazed}, booktitle = {IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)}, keywords = {itegpub}, pages = {57–60}, publisher = {IEEE}, title = {Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility}, year = 2022 }
%0 Conference Paper %1 ali2022heterogeneous %A Ali, Mohammad Wazed %B IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) %D 2022 %I IEEE %P 57--60 %R 10.1109/ACSOSC56246.2022.00030 %T Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility %U https://ieeexplore.ieee.org/document/9935088 %X 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.
Ali, M.W.: Deep Adaptive Knowledge-Aware Learning for E-Mobility In: Organic Computing - Doctoral Dissertation Colloquium 2022. kassel university press (2022).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently. They are in high demand, and EV usage is also rising. However, there is no fixed charging behavior or energy demand for these different types of EV users. Moreover, most renewable energy sources are stochastic and highly dependent on the weather. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep Learning (DL) 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, 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 article 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.).
@incollection{ali2022deep, abstract = {Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently. They are in high demand, and EV usage is also rising. However, there is no fixed charging behavior or energy demand for these different types of EV users. Moreover, most renewable energy sources are stochastic and highly dependent on the weather. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep Learning (DL) 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, 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 article 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.).}, author = {Ali, Mohammad Wazed}, booktitle = {Organic Computing - Doctoral Dissertation Colloquium 2022}, keywords = {itegpub}, note = {(accepted)}, publisher = {kassel university press}, title = {Deep Adaptive Knowledge-Aware Learning for E-Mobility}, year = 2022 }
%0 Book Section %1 ali2022deep %A Ali, Mohammad Wazed %B Organic Computing - Doctoral Dissertation Colloquium 2022 %D 2022 %I kassel university press %T Deep Adaptive Knowledge-Aware Learning for E-Mobility %X Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently. They are in high demand, and EV usage is also rising. However, there is no fixed charging behavior or energy demand for these different types of EV users. Moreover, most renewable energy sources are stochastic and highly dependent on the weather. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep Learning (DL) 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, 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 article 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.).