M. Sc. Mohammad Wazed Ali

AI for Computationally Intelligent Systems (AI4CIS)

Email: wazed.ali[at]uni-kassel[dot]de

Publications

[ 2024 ]
[ 2022 ]

2024[ to top ]

Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network. Ali, Mohammad Wazed; Mustafa, Mohammed Asif bin; Shuvo, Md. Aukerul Moin; Sick, Bernhard. In arXiv e-prints, bl arXiv:2409.11862. 2024.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62%, our proposed deep MQ-TCN model exhibited a remarkable 28.93% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88% PICP score for the load forecasting task at the NREL site using only two weeks of data.
@article{ali2024location, abstract = {Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62%, our proposed deep MQ-TCN model exhibited a remarkable 28.93% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88% PICP score for the load forecasting task at the NREL site using only two weeks of data.}, author = {Ali, Mohammad Wazed and Mustafa, Mohammed Asif bin and Shuvo, Md. Aukerul Moin and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2409.11862}, title = {Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network.}, year = 2024 }
%0 Journal Article %1 ali2024location %A Ali, Mohammad Wazed %A Mustafa, Mohammed Asif bin %A Shuvo, Md. Aukerul Moin %A Sick, Bernhard %D 2024 %J arXiv e-prints %P arXiv:2409.11862 %T Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network. %U https://arxiv.org/abs/2409.11862 %X Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62%, our proposed deep MQ-TCN model exhibited a remarkable 28.93% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88% PICP score for the load forecasting task at the NREL site using only two weeks of data.

2022[ to top ]

Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility. Ali, Mohammad Wazed. In IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), bll 57–60. IEEE, 2022.
- [ 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.
Deep Adaptive Knowledge-Aware Learning for E-Mobility. Ali, Mohammad Wazed. In Organic Computing - Doctoral Dissertation Colloquium 2022, S. Tomforde, C. Krupitzer (reds.), bll 139–155. 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 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.
@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 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.}, author = {Ali, Mohammad Wazed}, booktitle = {Organic Computing - Doctoral Dissertation Colloquium 2022}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {139–155}, 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 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 139--155 %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 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.