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Md. Shadman Abid

Nanotechnology Research Center, Sultan Qaboos University, Al-Khoud 123, Muscat, Oman.

Bio

Md. Shadman Abid is currently employed as a research assistant at the Nanotechnology Research Center of Sultan Qaboos University (SQU), where he is involved in multiple projects, including vision-informed multi-modal AI framework for active distribution network scheduling, AI-driven power grid path prediction, and deep learning-based geospatial mapping for energy distribution networks. He previously worked at the Sustainable Energy Research Center at SQU, where he developed advanced robust optimization and reinforcement learning-based models for EV charging infrastructure planning, focusing on the techno-economic, environmental, and operational aspects of microgrid management. Shadman additionally served as a part-time lecturer in the Department of Electrical and Electronic Engineering (EEE) at Sonargaon University, Dhaka, Bangladesh. He graduated in 2022 with a B.Sc. degree in EEE from the Islamic University of Technology (IUT), Gazipur, Bangladesh. His research interests span artificial intelligence, machine learning, deep learning, reinforcement learning, and optimization, with applications in sustainable energy systems, intelligent infrastructure planning, and data-driven decision-making. His long-term goal is to advance the development of intelligent cyber-physical systems that combine AI-driven learning methods with large-scale infrastructure applications.

Experiences

Nanotechnology Research Center, SQU | January 2024- Current

Sustainable Energy Research Center, SQU | January 2023- December 2023

Lecturer (Part-time), Sonargaon University, Dhaka | August 2022- June 2024

  • Courses include EEE 308: Power System I, EEE 309: Power System I Laboratory, EEE 407: Power System II, EEE 410: Power Plant Engineering, EEE 401: Control System I, EEE 402: Control System I Laboratory, EEE 165: Basics of Electrical Technology, and EE 1201: Electrical Engineering Principles.
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Current Projects

Graphical Abstract

Vision-Informed Multi-Modal Multi-Agent Reinforcement Learning for Active Distribution Network Scheduling

  • Developed a multi-modal, multi-agent reinforcement learning-based active distribution network scheduling framework where agents controlling energy storage systems, flexible loads, photovoltaic units, and static VAR compensators learn cooperative operational policies. A hybrid CNN–Vision Transformer (CNN-ViT) encoder was designed to extract solar panel soiling loss features from high-resolution images, which were then fused with real-time electrical grid measurements to form a perception-control feedback loop for informed scheduling decisions.
  • Defined primary objectives to simultaneously reduce operational costs, minimize network power losses, cut carbon emissions, and enhance grid flexibility, thereby ensuring resilient and sustainable operation of renewable-rich distribution systems.
  • Implemented a Graph-Attentive Actor–Critic network in PyTorch 2.6, trained on an NVIDIA Tesla A100-SXM4 GPU. The framework was validated using the DeepSolarEye dataset (44K annotated solar images) for soiling regression and applied to the realistic 2289-bus Nizwa distribution network in Oman. The system incorporated a 400 MW photovoltaic plant, 300 MWh energy storage system, 20 MVAR static VAR compensator, and six flexible water-pumping station loads, collected from Mazoon Electricity Company (MZEC).
DOI
Graphical Abstract

Deep Learning-based Geospatial Mapping Framework for Large-Scale Electric Power Grids

  • Proposed a residual Graph Convolutional Network (GCN) with a hierarchical attention mechanism for geospatial mapping of large-scale power grids, addressing challenges of data sparsity, scalability, and complex interdependencies. The framework integrates residual connections with local, global, and co-attention layers, enabling efficient representation of both micro-level and macro-level grid patterns for classification and regression tasks.
  • Set key objectives to accurately predict the geographic locations and connectivity of critical infrastructure components—such as poles, electricity service points, and substations—while reducing computational burden and enhancing scalability. The model aims to advance GIS-based power system mapping for reliable planning, operation, and sustainable infrastructure development.
  • Implemented the proposed architecture in PyTorch with Torch Geometric 2.6.1, trained using an NVIDIA Tesla A100-SXM4 GPU. The study utilized the Mazoon Electricity Company (MZEC) dataset comprising over 500K poles, 385K service points, and 23K substations across four Omani governorates, and the Nigerian transmission grid dataset containing 56K components. All processed datasets were made publicly available via Zenodo for open research access.
DOI
Graphical Abstract

AI-Driven Power Grid Path Prediction for Large-Scale Distribution Networks

  • Introduced a transformer-based deep learning approach that formulates power grid path prediction as a multi-output regression task rather than a conventional link-prediction classification. The model predicts continuous geographical coordinates of overhead conductor routes represented as multi-line strings, thereby reconstructing the full spatial trajectory of distribution lines.
  • Defined the objectives as improving geospatial accuracy of conductor path predictions, enhancing grid resilience by enabling precise mapping of infrastructure, and reducing uncertainty in route estimation. This novel regression-based formulation directly supports utilities in addressing the lack of detailed GIS databases in developing regions.
  • Designed and trained a transformer encoder architecture with multi-head self-attention and residual connections, complemented by feed-forward networks with GeLU activation and L2 regularization for stable learning. Implementation was carried out in TensorFlow 2.18.0 with Keras 3.10.0, ensuring efficient handling of high-dimensional sequential coordinate data.
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Graphical Abstract

Robust Deep Learning Model for Spatiotemporal Forecasting of Renewable Energy Sources

  • Developed a robust spatiotemporal forecasting framework for renewable energy generation, termed as the CNN–BiLSTM with Spatiotemporal Attention (CNN-BiLSTM-STA) model. The framework integrates CNNs for spatial feature extraction with bidirectional LSTM layers for temporal sequence learning, while a novel attention mechanism adaptively emphasizes critical spatial regions and time steps to improve multi-horizon forecasting of photovoltaic (PV) and wind power (WP) outputs.
  • Defined primary objectives to enhance the accuracy and robustness of renewable energy forecasts by (i) jointly predicting PV and WP production across multiple plants and time horizons, (ii) addressing challenges of high-dimensional spatiotemporal data without requiring site-specific models or heavy preprocessing, and (iii) ensuring resilience against data contamination scenarios such as missing values, Gaussian noise, outliers, and mixed disturbances.
  • Implemented the proposed spatiotemporal forecasting framework in TensorFlow 2.18.0 with Keras 3.8.0. The model was trained using an NVIDIA Tesla A100-SXM4 GPU, with extensive preprocessing, sequence construction, and data augmentation applied to high-resolution solar and wind datasets. Processed datasets were made publicly available via Zenodo for open research access.
DOI
Graphical Abstract

Neural Architecture Search via Reinforcement Learning for Image-based Solar Panel Soiling Loss Prediction

  • Proposed the first image-only regression framework for predicting soiling-induced power loss in solar panels using a reinforcement learning–driven neural architecture search (NAS) approach, eliminating the need for auxiliary time-series environmental data.
  • Evaluated the proposed NAS framework through two case studies: continuous regression of soiling-induced power loss and multi-class classification of soiling severity levels
  • Employed the DeepSolarEye dataset dataset with 44K+ annotated solar panel images; implemented a robust preprocessing pipeline to provide consistent inputs for CNN-based regression and classification tasks. Conducted extensive experiments on PyTorch 2.8.0+cu126, benchmarking against state-of-the-art NAS methods.
  • The work is currently under review.
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Featured Publications

Md. Shadman Abid, H. J. Apon, S. Hossain, A. Ahmed, R. Ahshan

A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning
Applied Energy, Volume 353, Part A, 2024, 122029, ISSN 0306-2619
Publication Type: Journal Article
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R. Ahshan, Md. Shadman Abid, M. Al-Abri

Multi-modal multi-task artificial intelligence model for active distribution network scheduling with multi-agent reinforcement learning
Electric Power Systems Research, Volume 250, 2026, 112091, ISSN 0378-7796,
Publication Type: Journal Article
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Md. Shadman Abid, R. Ahshan, R. Al-Abri, A. Al-Badi, M. Albadi

Techno-economic and environmental assessment of renewable energy sources, virtual synchronous generators, and electric vehicle charging stations in microgrids
Applied Energy, Volume 353, Part A, 2024, 122028, ISSN 0306-2619
Publication Type: Journal Article
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R. Ahshan, Md. Shadman Abid, M. Al-Abri

Geospatial Mapping of Large-Scale Electric Power Grids: A Residual Graph Convolutional Network-Based Approach with Attention Mechanism
Energy and AI, Volume 20, 2025, 100486, ISSN 2666-5468,
Publication Type: Journal Article
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