Department of Electrical Engineering and Computer Science

Permanent URI for this collectionhttps://hdl.handle.net/10315/30511

Browse

Now showing 1 - 20 of 26

Access status: Open Access ,
The reinvention of an energy hub’s planning by considering tidal turbine, bi-facial PV panels, and INVELOX: A unique outlook on the combination performance of novel RERs
(IEEE, 2024-11-26) Shaterabadi, Mohammad; Mehrjerdi, Hasan; Karimi, Houshang; Iqbal, Atif; Jirdehi, Mehdi Ahmadi
New approaches must be taken due to the rapid growth of societies globally and the urgent need for more energy, climate change, and global warming. One of these approaches is supporting and expanding new and efficient renewable energy resources (RERs). Therefore, this paper is dedicated to the energy planning and management of the proposed structure in which novel renewable-based technologies such as INVELOX wind turbines, Bi-facial photovoltaic (PV) panels, and tidal turbines (TT), in addition to the demand response programs (DRP) especially time of use (TOU), are investigated. The purpose is to simultaneously reducing the presented system’s overall cost and environmental pollution. The problem is modeled as mixed-integer linear programming (MILP) in GAMS software and solved using a CPLEX solver. The final results show that novel technologies recognized as efficient can significantly reduce contamination, expenses, and greenhouse gas (GHG) emissions while supplying our rapidly growing demand.
Access status: Open Access ,
The energy management and planning of the proposed Water-Energy nexus concept by considering tidal turbine and INVELOX as novel options
(IEEE, 2024-11-08) Shaterabadi, Mohammad; Mehrjerdi, Hasan; Dehghanian, Payman; Karimi, Houshang
The energy and water crisis due to the rapidly growing population and developing societies caused countries and governments to think about novel technologies and ways to respond to this issue. Therefore, this paper is about energy planning and management of the proposed concept for supplying water and energy simultaneously. This approach helps to overcome and supply the dramatic energy need of desalination and distribution systems. This article aims to supply the demands and minimize the total cost at the same time. Various renewable and non-renewable energy resources such as tidal turbines, INVELOX turbines, CHP, micro-turbines, and other elements are utilized in the proposed planning. Real weather conditions are considered to compute the output power of wind and tidal turbines. The presented water-energy nexus structure is modeled as a MINLP concept and solved in GAMS software using various solvers to illustrate the reliability of the optimization answers. The final results show a high improvement in the total cost reduction.
Access status: Open Access ,
The impact of tie line capacity on the energy planning of the multi-lateral grid by considering Bi-facial PV
(IEEE, 2026-02-03) Shaterabadi, Mohammad; Mehrjerdi, Hasan; Karimi, Houshang
The global energy landscape faces the pressing challenges of resource scarcity and environmental degradation due to fossil fuel dependency. This study investigates the integration of Bi-facial Photovoltaic (BPV) panels within a multi-lateral grid framework, emphasizing their impact on cost, profit, and pollution. By employing a multi-objective optimization approach, the problem is modeled as Mixed Integer Linear Programming (MILP) and solved using GAMS with advanced solvers like CPLEX and Baron. Four distinct scenarios are analyzed to evaluate the proposed energy strategy. The findings highlight the substantial benefits of BPV integration, demonstrating optimized values: a total cost of $19.751, a profit of $270.628, and a pollution level of 1003.268 kg. Furthermore, increasing tie line capacity significantly enhances economic outcomes without compromising environmental standards, yielding a recalibrated profit of $44.269 for building 1, a reduced cost of $265.671 for building 2, and a maintained pollution level. These results underscore the critical role of grid infrastructure optimization in balancing economic efficiency and sustainability. This research provides a novel perspective on renewable energy planning, showcasing BPV panels as a cost-effective, environmentally advantageous solution. It also highlights the strategic importance of enhancing transmission infrastructure to improve economic returns while maintaining ecological responsibility. The insights presented offer a robust framework for academics, policymakers, and industry stakeholders, advancing sustainable energy system optimization and long-term energy resilience.
Access status: Open Access ,
An Adaptive Kalman-Guided Soft Sensor Using Feedforward Neural Networks for SOC Estimation in Lithium-Ion Batteries
(IEEE, 2025) Mahdi Yousef, Mostafa; Shaterabadi, Mohammad; Karimi, Houshang
Accurate State-of-Charge (SOC) estimation is crucial for ensuring the battery’s safe operation and prolonged lifespan, but it remains a challenge due to sensor noise and system nonlinearity. This paper proposes a novel covariance-adaptive hybrid approach that interprets neural network outputs as virtual measurements and dynamically integrates them with the a priori state predictions from a Discrete Kalman filter to yield refined a posteriori estimates. The introduced adaptive scheme uses exogenous inputs derived from spectral analysis to provide past information to the Feedforward Neural Network (FNN) to perform soft sensing and generate network-aided measurements. The novel processing occurs after this step by adaptive integration with Kalman filter results. The developed hybrid architecture is tested on real experimental battery datasets for LG 18650 HG2, and the results prove its superior performance against existing state-of-the-art algorithms. Experimental results averaged over twenty Monte Carlo trials demonstrated nearly 28% and 39% improvement in the mean absolute error and root mean square error, respectively, compared to the baseline results.
Access status: Open Access ,
Enhancing Memory-Limited Feedforward Neural Networks for State of Charge Estimation through Temporal Feature Engineering
(IEEE, 2025-08-25) Mahdi Yousef, Mostafa; Shaterabadi, Mohammad; Karimi, Houshang
State-of-charge (SOC) estimation is a key function of Battery Management Systems (BMS) in electric vehicles and battery energy storage systems. However, SOC is not directly measurable, making accurate estimation inherently challenging. Data-driven approaches offer a practical solution by leveraging measurable inputs such as voltage, current, and temperature. Feedforward Neural Networks (FNNs) are attractive due to their low computational complexity, but they lack inherent temporal memory, unlike recurrent architectures. This paper investigates three established casual smoothing techniques-moving average, Butterworth filtering, and exponential moving average-as temporal memory proxies for enhancing FNN-based SOC estimation. Their effectiveness is supported by frequency-domain analysis using the Fast Fourier Transform (FFT), which reveals that key signal dynamics occur at ultra-low frequencies (less than 0.1 mHz), justifying the use of smoothing as memory-preserving transformations. The main contribution of this work is a unified, frequency-informed evaluation framework that systematically benchmarks these techniques under consistent conditions and across varying temperatures. All models are trained and evaluated on LG 18650HG2 Lithium-ion battery data, with 20 repeated runs per model to ensure statistical robustness.
Access status: Open Access ,
Advanced Energy Management and Planning Strategies for the IEEE 33-Bus System: Integration of EV Penetration, INVELOX Turbines, and Bifacial PV Panels
(IEEE, 2026-04-09) Shaterabadi, Mohammad; Mehrjerdi, Hasan; Karimi, Houshang
The rapid urbanization and growing population in cities present significant challenges in energy management. Smart cities aim to address these challenges by leveraging advanced technologies to optimize energy production, distribution, and consumption. This paper explores advanced energy management and planning strategies for the IEEE 33-Bus system through the synergistic integration of Electric Vehicle (EV) penetration, INVELOX turbines (IWT), and bifacial photovoltaic (BPV) panels. These innovative technologies offer substantial potential for enhancing power distribution systems’ efficiency, reliability, and sustainability. The integration of these elements is examined to understand their combined impact on energy management, system stability, and overall grid performance. The findings provide a comprehensive understanding of how these technologies can be harnessed to optimize energy management in modern power systems. Multi-objective functions, including total cost and emission, are considered, which should be minimized simultaneously. The study models a mixed integer quadratic linear programming (MIQCP) in GAMS software and solves it using CPLEX. Furthermore, the weighted sum approach is used to solve this problem. The final results show the efficiency and accuracy of responses and the operator’s planning in addressing the distribution network (DN) issues.
Access status: Open Access ,
Optimized Volt/VAR Control for Inverter-Interfaced Distributed Energy Resources in Compliance with IEEE 1547 Standards
(Institute of Central Computation and Knowledge, 2025-06-05) Shaterabadi, Mohammad
The increasing integration of distributed energy resources (DERs), such as photovoltaic(PV) systems and battery storage, into distribution networks necessitates advanced inverter controls to maintain stable grid operations. IEEE Standard 1547 permits smart inverter functionalities, including Volt/VAR control, enabling DERs to autonomously manage voltage fluctuations caused by varying load and generation conditions. However, configuring these Volt/VAR settings optimally is challenging, as default parameters provided by standards may not ensure optimal performance or dynamic stability. This paper proposes a customized, per-node Volt/VAR control optimization framework for single-phase distribution feeders, adapting inverter control parameters based on expected short-term load and solar generation patterns. Leveraging a projected gradient descent optimization approach, proposed methodology guarantees dynamic stability by approximating feasible operational parameters within a convex polytope and ensuring full compliance with IEEE 1547 voltage and reactive power specifications. Comprehensive numerical evaluations conducted on the IEEE 141-bus distribution system using realistic operational data demonstrate the effectiveness and practicality of the proposed method, highlighting improved voltage stability and regulation compared to conventional approaches.
Access status: Open Access ,
Stochastic Optimal Energy Planning of the Multi-connected Grids by the Presence of Bi-facial PV Panels: Interaction of Micro-nano and Main Grid
(Institute of Central Computation and Knowledge, 2025-12-10) Shaterabadi, Mohammad
The increasing greenhouse gas (GHG) emissions from fossil fuel-based energy systems have accelerated the global push toward cleaner technologies. Bi-facial photovoltaic (BPV) panels, capable of capturing solar irradiance from both sides, have emerged as a promising solution due to their higher energy yield and comparable costs to traditional PV systems. This paper explores the integration of BPV panels into a multi-connected grid comprising nano-, micro-, and main grid layers. A stochastic optimization framework is developed to address the uncertainties of solar irradiance. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model and solved using the Augmented Epsilon Constraint (AEC) method in the General Algebraic Modeling System (GAMS) environment. Results demonstrate that incorporating BPV panels reduces microgrid operational costs by approximately 20%, boosts nano-grid profits by about 81%, and cuts emissions by about 10%, highlighting their potential to enhance system efficiency, flexibility, and sustainability.
Access status: Open Access ,
Over-the-Air FEEL with Integrated Sensing: Joint Scheduling and Beamforming Design
(Institute of Electrical and Electronics Engineers, 2025-01-22) Asaad, Saba; Wang, Ping; Tabassum, Hina
Employing wireless systems with dual sensing and communications functionalities is becoming critical in next generation of wireless networks. In this paper, we propose a robust design for over-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We first derive novel expressions for the Cramér-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure radar sensing and model aggregation quality, respectively. Then, we develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while keeping the sensing and communication quality, determined respectively in terms of Cramér-Rao bound and achievable downlink rate, in a desired range. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm used widely for sparse recovery in the literature of compressed sensing. The proposed algorithm uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality of the system. It further invokes alternating optimization scheme to iteratively update the downlink beamforming and uplink post-processing by marginally optimizing them in each iteration. Convergence and complexity analysis of the proposed algorithm is presented. Numerical evaluations on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our proposed algorithm. The results show that by leveraging accurate sensing, the target echoes on the uplink signal can be effectively suppressed, ensuring the quality of model aggregation to remain intact despite the interference.
Access status: Open Access ,
Generalized Multi-Objective Reinforcement Learning With Envelope Updates in URLLC-Enabled Vehicular Networks
(Institute of Electrical and Electronics Engineers, 2025-06-17) Yan, Zijiang; Tabassum, Hina
We develop a novel multi-objective reinforcement learning (MORL) framework to jointly optimize wireless network selection and autonomous driving policies in a multi-band vehicular network operating on conventional sub-6 GHz spectrum and Terahertz frequencies. The proposed framework is designed to (i) maximize the traffic flow and minimize collisions by controlling the vehicle's motion dynamics (i.e., speed and acceleration), and (ii) enhance the ultra-reliable low-latency communication (URLLC) while minimizing handoffs (HOs). We cast this problem as a multi-objective Markov Decision Process (MOMDP) and develop solutions for both predefined and unknown preferences of the conflicting objectives. Specifically, we develop a novel envelope MORL solution which develops policies that address multiple objectives with unknown preferences to the agent. While this approach reduces reliance on scalar rewards, policy effectiveness varying with different preferences is a challenge. To address this, we apply a generalized version of the Bellman equation and optimize the convex envelope of multi-objective Q values to learn a unified parametric representation capable of generating optimal policies across all possible preference configurations. Following an initial learning phase, our agent can execute optimal policies under any specified preference or infer preferences from minimal data samples. Numerical results validate the efficacy of the envelope-based MORL solution and demonstrate interesting insights related to the inter-dependency of vehicle motion dynamics, HOs, and the communication data rate. The proposed policies enable autonomous vehicles (AVs) to adopt safe driving behaviors with improved connectivity.
Access status: Open Access ,
Resource Allocation in Cooperative Mid-Band/THz Networks in the Presence of Mobility
(Institute of Electrical and Electronics Engineers, 2025-10-08) Saeidi, Mohammad Amin; Tabassum, Hina
This paper develops a comprehensive framework to investigate and optimize the downlink performance of cooperative multi-band networks (MBNs) operating on upper mid-band (UMB) and terahertz (THz) frequencies, where base stations (BSs) in each band cooperatively serve users. The framework captures sophisticated features such as near-field channel modeling, fully and partially connected antenna architectures, and users’ mobility. First, we consider joint user association and hybrid beamforming optimization to maximize the system sum-rate, subject to power constraints, maximum cluster size of cooperating BSs, and users’ quality-of-service (QoS) constraints. By leveraging fractional programming FP and majorization-minimization techniques, an iterative algorithm is proposed to solve the non-convex optimization problem. We then consider handover (HO)-aware resource allocation for moving users in a cooperative UMB/THz MBN. Two HO-aware resource allocation methods are proposed. The first method focuses on maximizing the HO-aware system sum-rate subject to HO-aware QoS constraints. Using Jensen’s inequality and properties of logarithmic functions, the non-convex optimization problem is tightly approximated with a convex one and solved. The second method addresses a multi-objective optimization problem to maximize the system sum-rate, while minimizing the total number of HOs. Numerical results demonstrate the efficacy of the proposed algorithms, cooperative UMB/THz MBN over stand-alone THz networks, as well as the critical importance of accurate near-field modeling in extremely large antenna arrays. Moreover, the proposed HO-aware resource allocation methods effectively mitigate the impact of HOs, enhancing performance in the considered system.
Access status: Open Access ,
EMForecaster: A Deep Learning Framework for Time Series Forecasting in Wireless Networks With Distribution-Free Uncertainty Quantification
(Institute of Electrical and Electronics Engineers, 2025-07-24) Mootoo, Xavier Stephen; Tabassum, Hina; Chiaraviglio, Luca
With the recent advancements in wireless technologies, forecasting electromagnetic field (EMF) exposure has become increasingly critical to enable proactive network spectrum and power allocation, as well as network deployment planning. In this paper, we develop a deep learning (DL)-empowered time series forecasting framework referred to as EMForecaster. The proposed DL architecture employs patching to process temporal patterns at multiple scales, complemented by reversible instance normalization and mixing operations along both temporal and patch dimensions for efficient feature extraction. We then augment EMForecaster with a conformal prediction mechanism, which is independent of the data distribution, to enhance the trustworthiness of model predictions through uncertainty quantification of forecasts. In particular, the conformal prediction mechanism ensures that the ground truth lies within a prediction interval with target error rate α, where 1−α is referred to as coverage. However, a trade-off exists, as increasing coverage often results in wider prediction intervals. To address this challenge, we propose a new metric referred to as Trade-off Score, that balances the trustworthiness of the forecast (i.e., coverage) and the width of prediction interval. Our empirical evaluation demonstrates that EMForecaster achieves superior performance across diverse EMF datasets, spanning both short-term and long-term prediction horizons. In point forecasting tasks, EMForecaster substantially outperforms current state-of-the-art DL approaches, showing improvements of 53.97% over the Transformer architecture and 38.44% over the average of all baseline models. In terms of conformal prediction performance, EMForecaster exhibits excellent balance between prediction interval width and coverage, as measured by the coverage-width tradeoff score. This balance is comparable to DLinear's performance while showing marked improvements of 24.73% over the average baseline and 49.17% over the Transformer architecture.
Access status: Open Access ,
Method and apparatus for distributing traffic load between different communication cells
(2023-11-21) Wu, Di; Kang, Jikun; Xu, Yi Tian; Li, Jimmy; Jenkin, Michael R; Liu, Xue; Chen, Xi; Dudek, Gregory Lewis; Park, Intaik; Lee, Taesop
An apparatus distributing communication load over a plurality of communication cells may select action centers from random cell reselection values, based on a standard deviation of an internet protocol (IP) throughout over the plurality of communication cells; input a first vector indicating a communication state of a communication system and a second vector indicating the standard deviation of the IP throughout of the plurality of communication cells, to a neural network to output a sum of the action centers and offsets as cell reselection parameters; and transmit the cell reselection parameters to the communication system to enable a base station of the communication system to perform a cell reselection based on the cell reselection parameters.
Access status: Open Access ,
System and method for rendering of an animated avatar
(2019-11-09) Jenkin, Michael R; Tarawneh, Enas
There are provided systems and methods for rendering of an animated avatar. An embodiment of the method includes: determining a first rendering time of a first clip as approximately equivalent to a predetermined acceptable rendering latency, a first playing time of the first clip determined as approximately the first rendering time multiplied by a multiplicative factor; rendering the first clip; determining a subsequent rendering time for each of one or more subsequent clips, each subsequent rendering time is determined to be approximately equivalent to the predetermined acceptable rendering latency plus the total playing time of the preceding clips, each subsequent playing time is determined to be approximately the rendering time of the respective subsequent clip multiplied by the multiplicative factor; and rendering the one or more subsequent clips.
Access status: Open Access ,
Exploiting Reward Machines with Deep Reinforcement Learning in Continuous Action Domains
(Springer Cham, 2023-09-07) Haolin Sun; Lesperance, Yves
In this paper, we address the challenges of non-Markovian rewards and learning efficiency in deep reinforcement learning (DRL) in continuous action domains by exploiting reward machines (RMs) and counterfactual experiences for reward machines (CRM). RM and CRM were proposed by Toro Icarte et al. A reward machine can decompose a task, convey its high-level structure to an agent, and support certain non-Markovian task specifications. In this paper, we integrate state-of-the-art DRL algorithms with RMs to enhance learning efficiency. Our experimental results demonstrate that Soft Actor-Critic with counterfactual experiences for RMs (SAC-CRM) facilitates faster learning of better policies, while Deep Deterministic Policy Gradient with counterfactual experiences for RMs (DDPG-CRM) is slower, achieves lower rewards, but is more stable. Option-based Hierarchical Reinforcement Learning for reward machines (HRM) and Twin Delayed Deep Deterministic (TD3) with CRM generally underperform compared to SAC-CRM and DDPG-CRM. This work contributes to the ongoing development of more efficient and robust DRL approaches by leveraging the potential of RMs in practical problem-solving scenarios.
Access status: Open Access ,
Large-scale, touch-sensitive video display
(2000-09-12) Jenkin, Michael R; Tsotsos, John K
A video surface is constructed by adjoining a large number of flat screen display devices together. Each screen on this surface is controlled by its own computer processor and these processors are networked together. Superimposed over this surface is a tiling of transparent touch-sensitive screens which allow for user input. The resulting display is thin, has a very high resolution, appears to be a single large screen to the user, and is capable of supporting many different types of human-machine interaction.
Access status: Open Access ,
Decentralized Topology Reconfiguration in Multiphase Distribution Networks
(IEEE Transactions on Signal and Information Processing over Networks, 2019-02) Srikantha, P.; Liu, J.
The cyber-physical nature of the modern power grid allows active power entities to exchange information signals with one another to make intelligent local actuation decisions. Exacting effective coordination amongst these cyber-enabled entities by way of strategic signal exchanges is essential for accommodating highly fluctuating power components (e.g., renewables, electric vehicles, etc.) that are becoming prevalent in today's electric grid. As such, in this paper, we present a novel decentralized topology reconfiguration algorithm for the distribution network (DN) that allows the system to adapt in real time to unexpected perturbations and/or congestions to restore balance in loads across the feeder and improve the DN voltage profile. For this, individual agents residing in DN buses iteratively exchange signals with neighbouring nodes to infer the current state (e.g., power balance and voltage) of the system and utilize this information to make local line switching decisions. Strong convergence properties and optimality conditions of the proposed algorithm are established via theoretical studies evoking potential games and discrete concavity. Comparative simulation studies conducted on realistic DNs showcase the practical properties of the proposed algorithm.
Access status: Open Access ,
Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks
(IEEE Transactions on Signal and Information Processing over Networks, 2018-07) srikantha, P.; Kundur, D.
Rapid advancements in smart grid technologies have brought about the proliferation of intelligent and actuating power system components such as distributed generation, storage, and smart appliance units. Capitalizing fully on the potential benefits of these systems for sustainable and economical power generation, management, and delivery is currently a significant challenge due to issues of scalability, intermittency, and heterogeneity of the associated networks. In particular, vertically integrated and centralized power system management is no longer tractable for optimally coordinating these diverse devices at large scale while also accounting for the underlying complex physical grid constraints. To address these challenges, we propose a hierarchical signal processing framework for optimal power flow management whereby the cyber-physical network relationships of the modern grid are leveraged to enable intelligent decision-making by individual devices based on local constraints and external information. Decentralized and distributed techniques based on convex optimization and game theoretic constructs are employed for information exchanges and decision-making at each tier of the proposed framework. It is shown via theoretical and simulation studies that our technique allows for the seamless integration of power components into the grid with low computational and communication overhead while maintaining optimal, sustainable, and feasible grid operations.
Access status: Open Access ,
Stealthy Black-box Attacks on Deep Learning Non-intrusive Load Monitoring Models
(IEEE Transactions on Smart Grid, 2021-03) Srikantha, P.; Wang, J.
With the advent of the advanced metering infrastructure, electricity usage data is being continuously generated at large volumes by smart meters vastly deployed across the modern power grid. Electric power utility companies and third party entities such as smart home management solution providers gain significant insights into these datasets via machine learning (ML) models. These are then utilized to perform active/passive power demand management that fosters economical and sustainable electricity usage. Although ML models are powerful, these remain vulnerable to adversarial attacks. A novel stealthy black-box attack construction model is proposed that targets deep learning models utilized to perform non-intrusive load monitoring based on smart meter data. These attacks are practical as there is no assumption of the knowledge of training data, internal parameters, and architecture of the targeted ML model. The profound impact of the proposed stealthy attack constructions on energy analytics and decision-making processes is shown through comprehensive theoretical, practical, and comparative analysis. This work sheds light on vulnerabilities of ML models in the smart grid context and provides valuable insights for securely accommodating increasing prevalence of artificial intelligence in the modern power grid.
Access status: Open Access ,
A Data-Driven Approach for Generating Synthetic Load Patterns and Usage Habits
(IEEE Transactions on Smart Grid, 2020-07) Pirathayini, Srikantha; S.E. Kababji
Today's electricity grid is rapidly evolving to become highly connected and automated. These advancements have been mainly attributed to the ubiquitous communication/computational capabilities in the grid and the Internet of Things paradigm that is steadily permeating modern society. Another trend is the recent resurgence of machine learning which is especially timely for smart grid applications. However, a major deterrent in effectively utilizing machine learning algorithms is the lack of labelled training data. We overcome this issue in the specific context of smart meter data by proposing a flexible framework for generating synthetic labelled load (e.g., appliance) patterns and usage habits via a non-intrusive novel data-driven approach. We leverage on recent developments in generative adversarial networks (GAN) and kernel density estimators (KDE) to eliminate model-based assumptions that otherwise result in biases. The ensuing synthetic datasets resemble real datasets and lend to rich and diverse training/testing platforms for developing effective machine learning algorithms pertaining to consumer-side energy applications. Theoretical and practical studies presented in this paper highlight the viability and superior performance of the proposed framework.

Browse

Recent Submissions