Habib Ullah Manzoor

EDUCATION

PhD scholar
MS in Electrical Engineering
BSc Electrical Engineering

RESEARCH INTERESTS

Machine learning, communication system, Optical Engineering, Renewable Energy, Solar cells, Fiber optics, free space optics

Scopus Publications

Optimization of indium concentration and compositional grading in InGaN heterojunction solar cells by SCAPS-1D simulation
Mohammed Kakasur Omar, Habib Ullah Manzoor, Sha Shiong Ng, Mohd Marzaini Mohd Rashid, and Mohd Zamir Pakhuruddin
IOP Publishing
Abstract Indium gallium nitride (InGaN) thin-film solar cells exhibit exceptional potential for photovoltaic (PV) applications due to their tunable bandgap (0.7–3.4 eV) and high absorption coefficient (>105 cm−1). Using SCAPS-1D simulation, this study demonstrates that incorporating compositional grading in the InGaN absorber layer significantly enhances the solar cells performance. Graded InGaN solar cells with optimized Indium (In) concentration achieve superior power conversion efficiency (PCE) compared to ungraded cells. The optimal graded structure, with an In concentration of 0.7, achieves PCE of 36.08%, current density (Jsc) of 23.52 mA cm−2, open-circuit voltage (Voc) of 1.66 V, and fill factor (FF) of 92.1%. In contrast, the ungraded cell exhibits PCE of 34.83%, Jsc of 22.8 mA cm−2, Voc of 1.66 V, and FF of 91.87%. These findings underscore the efficacy of compositional grading in advancing high-efficiency InGaN solar cells.


Integrated Fuzzy-Knapsack Based Demand Response Energy Management System for Smart Grid Buildings
Zulfiqar Memon, Fawad Azeem, Tareq Manzoor, and Habib Ullah Manzoor
Wiley
ABSTRACTDemand response schemes play a vital role in managing the load demand. However, the demand response applicability is pre‐descriptive where loads to be managed are pre‐selected majorly based on the availability of renewable energy and lower tariff rates. However, in hospitality buildings such as hotels, user comfort cannot be compromised by the cost of energy. The arrival of guests is a unique parameter that drives the load consumption regardless of the availability of free energy or lower tariff rates. During higher guest arrivals, pre‐descriptive loads meant to be scheduled during low renewable energy availability and higher tariff rates cannot be compromised over guest comfort. Similarly, pre‐descriptive loads that are already not in operation at the time of low guest arrivals will result in wastage of green power at times of its availability. There is a need to develop an automated demand response that has the liberty to select any load for shifting to renewable energy based on the power they consume to utilize maximum resources without compromising guest comfort. In this research, a novel automated demand response scheme is developed that intelligently selects any load from the building in real time while mapping it with the available capacity of renewable power. A cascaded fuzzy integrated knapsack algorithm is designed for intelligent selection of loads participation in demand response. Based on the availability of solar PV power, grid rates, and load operations, fuzzy designates values to the random operational loads. In the second step, the designated values are given to the Knapsack algorithm to find the best optimal responsive loads to be operated at that time. In the proposed approach, random loads were selected for shifting to renewable power without any prior load selection, which enhances the operation and usability of solar PV power. It was found that 88%–100% of solar PV power was utilized under all simulated scenarios of operation.


Effect of Optimized Tilt Angle of PV Modules on Solar Irradiance for Residential and Commercial Buildings in Different Cities of Pakistan: Simulation-Based Study
Habib Ullah Manzoor, Sheikh Muhammad Aaqib, Tareq Manzoor, Fawad Azeem, Muhammad Waqas Ashraf, and Sanaullah Manzoor
Wiley
ABSTRACTThe tilt angle of a solar PV panel is a critical factor in improving the efficiency of photovoltaic (PV) systems. While tracking systems can enhance performance, they are typically not cost‐effective for residential areas. Alternatively, setting an optimized fixed tilt angle or adjusting the tilt seasonally can mitigate power losses. This study evaluates optimal seasonal tilt angles and the corresponding solar radiation on PV panels for 10 major cities across Pakistan. A novel mathematical framework is proposed to calculate the optimal tilt angle using parameters such as alignment, azimuth, gradient, and temporal angles. Seasonal adjustments are shown to increase solar intensity from to during winter, significantly enhancing output power. For instance, an improvement of up to in output power density was observed at the Quaid‐e‐Azam Solar Park using crystalline solar cells. These findings demonstrate the potential for substantial improvements in solar power production through seasonally optimized tilt angles, particularly during the shorter winter days.


Exploring the Potential of Cross-Border Energy Trade in SAARC Countries for Achieving Sustainable Development Goals (SDGs)
Hassan Zidan, Maaz Tahir Malik, Usman Rafique, Fawad Azeem, Tareq Manzoor, and Habib Ullah Manzoor
Wiley
ABSTRACTSouth Asian Association for Regional Cooperation (SAARC) aims to develop a ring for sustainable generation of energy that caters for the needs of the member nations. Being a primarily underdeveloped region, the union of SAARC countries is facing a serious energy crisis, owing to rapid increase in population and industrialization. All the member countries predominantly rely upon imported fossil fuels for power generation. In line with the vision of SAARC, this research explores the potential of renewable energy and provides a quantitative cross‐border electricity trade assessment and its social‐economical‐technical (SET) impact on the SAARC region. The research presented in this article signifies the need for cross‐border electricity trade to fulfill the ever‐increasing demand‐supply gap in the region by providing a rudimentary framework. This approach has the viable potential for alleviating the substandard quality of life in the region. The paper highlights near‐border cities of SAARC countries that can potentially perform cross‐border electricity trade in the SAARC region. In the first phase, near‐border cities of the SAARC countries are highlighted. Moreover, as a part of social impact, this study analyzes the social needs of energy suppliers and receiving regions and maps it with the United Nations' sustainable development goals. The SDG mapping process is based on the societal needs of the supplier and receiver countries. The societal needs are assessed and mapped with the corresponding SDGs. Results reveal that India can potentially provide power to the neighboring countries through wind and solar power generating 125.9 million US dollars and providing 2485 GWh of energy which is 85% of the total generation in the SAARC region which is 2896.51 GWh. A total of 2.2 Ton/GWh of CO2 mitigation can be achieved through green generation whereas 13 Sustainable Development Goals (SDGs) can be achieved through social impacts between the energy trading countries.


Investigation of Thermal Management Capacity of Casson Electrolytes in Porous Electrodes in Lithium-Ion Battery Applications
Tareq Manzoor, S. Iqbal, Tauseef Anwer, Sanaullah Manzoor, Ghulam Mustafa, and Habib Ullah Manzoor
Wiley
ABSTRACTThe study of the Casson electrolyte in lithium‐ion batteries (LIBs) is important because of their complexities due to tougher operational conditions and other challenges during charging–discharging challenges with their improved thermal management capacity and enhanced safety. This further optimizes the thermal management avoiding chances of hot spots or thermal runaway, thereby making LIBs safer. In this investigation, convective loads for non‐Newtonian fluid as electrolyte Casson‐type boundary layer flow related to plate and flat surfaces in non‐Darcy permeable porous electrodes have been deliberated. We have employed the Optimal Homopotic Asymptotic Method technique to solve the equation of the system. The effects and influences of Casson factors, permeability, flow constraints, Prandtl values related to flow and thermal dissipation, and boundary layer profiles have been studied. From the results, it is concluded that thermal parameters and porousness have affected the system, and the upsurge in the porousness actually decreases heat transport effects and proportions. The results of this study are relevant to the development of more effective porous electrodes for achieving high performance with long cycle life. These studies help improve the utilization of mass and heat transfer properties, as affected by the non‐Newtonian behavior of the electrolyte, to help in the design of next‐generation LIBs with higher energy density along with fast charge/discharge rates.


Semantic-Aware Federated Blockage Prediction (SFBP) in Vision-Aided Next-Generation Wireless Network
Ahsan Raza Khan, Habib Ullah Manzoor, Rao Naveed Bin Rais, Sajjad Hussain, Lina Mohjazi, Muhammad Ali Imran, and Ahmed Zoha
Institute of Electrical and Electronics Engineers (IEEE)
—Predicting signal blockages in millimetre waves (mmWave) and terahertz (THz) networks is a challenging task that requires anticipating environmental changes. One promising solution is to use multi-modal data, such as vision and wireless inputs, and deep learning. However, combining these data sources can lead to higher communication costs, inefficient bandwidth usage, and undesirable latency, making it challenging. This paper proposes a semantic aware federated blockage prediction (SFBP) framework for a vision-aided next-generation wireless network. This framework uses computer vision techniques to extract semantic information from images and performs distributed on-device learning to enhance blockage prediction. Federated learning enables collaborative model training without exposing private data. Our proposed framework achieves 97.5% accuracy in predicting signal blockages, which is very close to the performance of centralised training. By using semantic information to train models, SFBP reduces communication costs by 88.75% and 57.87% compared to centralised learning and federated learning without semantics, respectively. On-device inference further reduces latency by 23% and 18% compared to centralised and federated learning without semantics, respectively.


Hybrid Neuromorphic-Federated Learning for Activity Recognition Using Multi-modal Wearable Sensors




Robustness Against Data Integrity Attacks in Decentralized Federated Load Forecasting
Attia Shabbir, Habib Ullah Manzoor, Muhmmand Naisr Manzoor, Sajjad Hussain, and Ahmed Zoha
MDPI AG
This study examines the impact of data integrity attacks on Federated Learning (FL) for load forecasting in smart grid systems, where privacy-sensitive data require robust management. While FL provides a privacy-preserving approach to distributed model training, it remains susceptible to attacks like data poisoning, which can impair model performance. We compare Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL), using line, ring and bus topologies, under adversarial conditions. Employing a three-layer Artificial Neural Network (ANN) with substation-level datasets (APEhourly,PJMEhourly, and COMEDhourly), we evaluate the system’s resilience in the absence of anomaly detection. Results indicate that DFL significantly outperforms CFL in attack resistance, achieving Mean Absolute Percentage Errors (MAPEs) of 0.48%, 4.29% and 0.702% across datasets, compared to the CFL MAPEs of 6.07%, 18.49% and 10.19%. This demonstrates the potential of DFL as a resilient, secure solution for load forecasting in smart grids, minimizing dependence on anomaly detection to maintain data integrity.


Evaluation of the potential power generation resources in SAARC region for sustainable energy trade
Usman Rafique, Hasan A. Zidan, Fawad Azeem, Sarang Amir, Tareq Manzoor, and Habib Ullah Manzoor
Wiley
AbstractThis article presents a comprehensive review on the contemporary condition of electrical energy in the SAARC countries with particular focus on conventional as well as renewable resources. The region lies at the center of the Asia with a dense population where demand and supply gap has always been a challenging factor for the governments. In addition, the region has observed enormous industrial growth during past two and half decades whose sustainability is totally dependent upon the continuous supply of energy. A bulk of valuable literature has been published in recent years that addresses the challenges, infrastructure improvements margins and several schemes to mitigate the energy deficiency in the region but most of it focuses upon either generation or transmission. This article covers this gap and presents to‐the‐point data that can be used for future forecasting, cross‐border trade possibilities and indigenous energy generation for remote and islanded regions within and across the SAARC mainland. A detailed review on electrical power generation potential by exploiting the renewable energy resources (RERs), generation capacity, installed capacity, energy short fall and transmission and distribution (T&D) losses is presented for each SAARC country and the results are presented in forms of tables and graphs. It can be inferred from the data presented in this paper that the energy crisis of the SAARC region can be overcome by mutual trade among the land‐connected countries by transporting the electrical energy, generated through RERs, across the borders. The paper concludes that the region has enormous potential for renewable energy available in form of hydal, solar, wind and biomass that, if maturely harnessed, can only not fulfil the local demands of the region but also be exported to establish the framework of cross‐border energy trade in future for sustainability of industrial and domestic utilization.


Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?
Habib Ullah Manzoor, Sajjad Hussain, David Flynn, and Ahmed Zoha
Elsevier BV



Adaptive Single-layer Aggregation Framework for Energy-efficient and Privacy-preserving Load Forecasting in Heterogeneous Federated Smart Grids
Habib Ullah Manzoor, Atif Jafri, and Ahmed Zoha
Elsevier BV



A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy
Habib Ullah Manzoor, Attia Shabbir, Ao Chen, David Flynn, and Ahmed Zoha
MDPI AG
Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling decentralized model training across multiple devices while preserving data privacy. However, the decentralized nature of FL introduces significant security challenges, making it vulnerable to various attacks targeting models, data, and privacy. This survey provides a comprehensive overview of the defense strategies against these attacks, categorizing them into data and model defenses and privacy attacks. We explore pre-aggregation, in-aggregation, and post-aggregation defenses, highlighting their methodologies and effectiveness. Additionally, the survey delves into advanced techniques such as homomorphic encryption and differential privacy to safeguard sensitive information. The integration of blockchain technology for enhancing security in FL environments is also discussed, along with incentive mechanisms to promote active participation among clients. Through this detailed examination, the survey aims to inform and guide future research in developing robust defense frameworks for FL systems.


Antimony Trisulfide with Graphene Oxide Coated Titania Nanotube Arrays as Anode Material for Lithium-ion Batteries
Tauseef Anwar, Tareq Manzoor, Naveed Hussain, Syed Nasir Shah, Shazia Perveen, Sana Ullah Asif, Farhat Nosheen, Abdul Jabbar Khan, and Habib Ullah Manzoor
Springer Science and Business Media LLC



Computational Optimization for CdS/CIGS/GaAs Layered Solar Cell Architecture
Satyam Bhatti, Habib Ullah Manzoor, Ahmed Zoha, and Rami Ghannam
MDPI AG
Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance compared to those utilizing cadmium sulfide (CdS). Likewise, CIGS-based devices are more efficient according to their device performance, environmentally benign nature, and thus, reduced cost. Therefore, the paper introduces an optimization process of three-layered n-CdS/p-CIGS/p-GaAs (NPP)) solar cell architecture based on thickness and carrier charge density. An in-depth investigation of the numerical analysis for homojunction PPN-junction with the ’GaAs’ layer structure along with n-ZnO front contact was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Subsequently, various computational optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture were examined. The electronic characteristics by adding the GaAs layer on the top of the conventional (PN) junction further led to optimized values of the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF), and short-circuit current density (JSC) of the solar cell. Lastly, the paper concludes by highlighting the most promising results of our study, showcasing the impact of adding the GaAs layer. Hence, using the optimized values from the analysis, thickness of 5 (μm) and carrier density of 1×1020 (1/cm) resulted in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16 V, 89.52%, and 43.88 (mA/m2), respectively, for the proposed solar cell architecture. The outcomes of the study aim to pave the path for highly efficient, optimized, and robust multi-junction solar cells.


Finite Element Approach for Rheological Behavior in Colloidal Electrolytes in Lithium-Ion Battery Performance
Ahsan Raza, Tareq Manzoor, Shaukat Iqbal, Tauseef Anwar, Adeel Ashraf, and Habib Ullah Manzoor
American Chemical Society (ACS)



Leveraging InGaN solar cells for visible light communication reception
Habib Ullah Manzoor, Sanaullah Manzoor, Muhammad Ali Jamshed, and Tareq Manzoor
Institution of Engineering and Technology (IET)
AbstractSolar cells are increasingly being utilised for both energy harvesting and reception in free‐space optical (FSO) communication networks. The authors focus on the implementation of a mid‐band p‐In0.01Ga0.99 N/p‐In0.5Ga0.5 N/n‐In0.5Ga0.5 N (PPN) solar cell, boasting an impressive 26.36% conversion efficiency (under 1.5AM conditions) as a receiver within an indoor FSO communication network. Employing a solar cell with dimensions of 1 mm in length and width, the FSO system underwent simulation using Optisystm software, while the solar cell's behaviour was simulated using SCAPS‐1D. The received power from the solar cell was then compared to that of four commercially available avalanche photodiode (APD) receivers. Exploring incident wavelengths spanning 400–700 nm within the visible spectrum, across transmission distances of 5, 10, 15, and 20 m, the study presented current‐voltage (IV) and power‐voltage curves. Notably, the InGaN solar cell exhibited superior electrical power output compared to all commercial APDs. In conclusion, the findings underscore that augmenting received power has the potential to enhance FSO network quality and support extended transmission distances.


Magnetic micro-fluidics in 3D microchannel at the micro-scale: Unlocking nano-porous electrode potential for lithium-ion micro-batteries
Adeel Ashraf, Tareq Manzoor, Shaukat Iqbal, Tauseef Anwar, Muhammad Farooq‐i‐Azam, Zeashan Khan, and Habib Ullah Manzoor
Wiley
AbstractEnhancing the nanosized‐electrolyte's characteristics in Lithium‐driven micro‐batteries (LIMBs) is indispensable to improve the overall efficiency, security, and lifespan of these energy devices, designing nano‐sized electrolyte with a wide electrochemical stability window while keeping them compatible with electrode materials is one of the improvement goals. Battery technologies must go through this optimization process in order to be used practically. A sensing mechanism to keep an eye on the health of Li‐ion energy devices through the magnetization. Magnetic micro‐fluidic patterns that change could be a sign of battery deterioration or other problems with performance. Li‐ion battery health is one application of magnetic sensing that you can do with magnetic sensing. Battery health variations and other performance problems can be found using magnetic mass transport patterns. Present study examines the effects of magnetic field on Eyring–Powel mass transport in nano‐porous channels over a stretching sheet. The principal equations exhibiting the phenomenon are transformed into non‐linear differential equation by second‐order approximation by using a similarity transformation. Furthermore, a semi‐analytic technique named optimal homotopy asymptotic method (OHAM) is used to solve the transformed Eyring–Powell model. The numerical results demonstrated the impact of variations in velocity, skin‐friction coefficient and Sherwood number for the proposed scheme.


Rethinking Federated Learning: An Adversarial Perspective on Global vs. Local Learning for Load Forecasting
Habib Ullah Manzoor, Attia Shabiar, Dinh C. Nguyen, Lina Mohjazi, Aryan Kaushik, and Ahmed Zoha
IEEE
Resilient federated learning (FL) systems are essential for accurate load forecasting, especially when under adversarial attacks. Since these systems aggregate decentralized data from various sources, they are particularly vulnerable to attacks that can undermine forecast accuracy and reliability. To enhance robustness in load forecasting, our study investigates methods for strengthening FL systems by optimizing the balance between global and local learning processes. This paper explores the trade-offs between global and local learning in federated load forecasting under adversarial conditions. We develop a neural network framework tailored for federated short-term load forecasting and assess its performance against model poisoning attacks. Our experiments demonstrate that increasing the number of local training epochs while reducing global communication rounds can significantly enhance model robustness. Specifically, when local epochs are increased from 1 to 10 and global epochs are decreased from 1000 to 100, the average client Mean Absolute Percentage Error (MAPE) decreases from 92.3 % to 4.3 % under attack conditions. This improvement stems from a reduced attack surface and the concept of catastrophic forgetting, where local models gradually mitigate adversarial effects through extended training on authentic data, providing valuable insights for the design of secure and efficient distributed energy forecasting systems.


Enhancing Consumer Privacy in Federated Load Forecasting Through Single Layer Aggregation
Habib Ullah Manzoor, Muhammad Ali Jamshed, Ahmed Zoha, and Sanaullah Manzoor
Institute of Electrical and Electronics Engineers (IEEE)



Swarm-Optimized ZnO/CdS/CIGS/GaAs Solar Cell for Enhanced Efficiency and Thermal Resilience
Habib Ullah Manzoor, Tareq Manzoor, Sajjad Hussain, Muhammad Nasir Manzoor, and Ahmed Zoha
Wiley
Optimizing solar cell design is vital for boosting efficiency, cutting production costs, and meeting the increasing demand for renewable energy solutions. Through meticulous adjustments in material compositions and device architectures, optimization enhances energy conversion efficiency, making solar power more competitive and adaptable across various applications. This article presents the optimization and efficiency enhancement of a ZnO/CdS/CIGS solar cell with GaAs. The optimization process utilizes the particle swarm optimization algorithm with a step‐by‐step approach. Solar cells are designed using SCAPS‐1D software, and optimization is performed using Python. The optimized ZnO/CdS/CIGS solar cell achieves an efficiency of 32.4%, which rises to 44.7% upon integrating a GaAs layer. Further efficiency gains are observed, reaching 53.2% through back contact optimization, providing a power density of 54 mW cm−2. Optimization also notices a significant improvement in quantum efficiency. The cells are tested under concentrated solar irradiance (1000–10 000 W m−2) and temperatures (300–800 K). Results show that at 10 000 W m−2 and 800 K, the ZnO/CdS/CIGS/GaAs cell requires 53.9% less material than the ZnO/CdS/CIGS cell. Thus, adding GaAs enhances efficiency and thermal resilience, making it ideal for concentrated photovoltaics.


Resilience of Federated Learning Against False Data Injection Attacks in Energy Forecasting
Attia Shabbir, Habib Ullah Manzoor, Ridha Alaa Ahmed, and Zahid Halim
IEEE
Federated learning (FL) has established itself as a communication-efficient, privacy-aware, and cost-effective technique for training machine learning models in energy forecasting. This approach enables simultaneous model training across multiple smart grids while keeping data decentralized at edge nodes. However, FL is not immune to backdoor adversarial attacks, such as data and model poisoning. In this paper, we scrutinize the impact of two data poisoning techniques: scaling and random noise effects. The attack was initiated on one client among ten. As the attack percentage increases, the Mean Absolute Percentage Error (MAPE) of the local model also rises. Our simulation results reveal that the scaling effect elevated MAPE from 0.193% to 32.72%, while random noise increased MAPE from 0.183% to 129.75% as the attacked percentage rose from 10% to 100%. It is concluded that data poisoning solely affects the local model and does not significantly impact the global model; hence, it can provide more resilience than centralized machine learning models.


A Privacy and Energy-Aware Federated Framework for Human Activity Recognition
Ahsan Raza Khan, Habib Ullah Manzoor, Fahad Ayaz, Muhammad Ali Imran, and Ahmed Zoha
MDPI AG
Human activity recognition (HAR) using wearable sensors enables continuous monitoring for healthcare applications. However, the conventional centralised training of deep learning models on sensor data poses challenges related to privacy, communication costs, and on-device efficiency. This paper proposes a federated learning framework integrating spiking neural networks (SNNs) with long short-term memory (LSTM) networks for energy-efficient and privacy-preserving HAR. The hybrid spiking-LSTM (S-LSTM) model synergistically combines the event-driven efficiency of SNNs and the sequence modelling capability of LSTMs. The model is trained using surrogate gradient learning and backpropagation through time, enabling fully supervised end-to-end learning. Extensive evaluations of two public datasets demonstrate that the proposed approach outperforms LSTM, CNN, and S-CNN models in accuracy and energy efficiency. For instance, the proposed S-LSTM achieved an accuracy of 97.36% and 89.69% for indoor and outdoor scenarios, respectively. Furthermore, the results also showed a significant improvement in energy efficiency of 32.30%, compared to simple LSTM. Additionally, we highlight the significance of personalisation in HAR, where fine-tuning with local data enhances model accuracy by up to 9% for individual users.


Revolutionizing Low-Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques
Satyam Bhatti, Habib Ullah Manzoor, Bruno Michel, Ruy Sebastian Bonilla, Richard Abrams, Ahmed Zoha, Sajjad Hussain, and Rami Ghannam
Wiley
Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low‐cost photovoltaic (PV) cells in miniaturized electronic devices. Indeed, ML is set to significantly contribute to the development of more efficient and cost‐effective solar cells. This systematic review offers an extensive analysis of recent ML techniques in designing novel solar cell materials and structures, highlighting their potential to transform the low‐cost solar cell research and development landscape. The review encompasses a variety of ML approaches, such as Gaussian process regression (GPR), Bayesian optimization (BO), and deep neural networks (DNNs), which have proven effective in boosting the efficiency, stability, and affordability of solar cells. The findings of this review indicate that GPR combined with BO is the most promising method for developing low‐cost solar cells. These techniques can significantly speed up the discovery of new PV materials and structures while enhancing the efficiency and stability of low‐cost solar cells. The review concludes with insights on the challenges, prospects, and future directions of ML in low‐cost solar cell research and development.


FedBranched: Leveraging Federated Learning for Anomaly-Aware Load Forecasting in Energy Networks
Habib Ullah Manzoor, Ahsan Raza Khan, David Flynn, Muhammad Mahtab Alam, Muhammad Akram, Muhammad Ali Imran, and Ahmed Zoha
MDPI AG
Increased demand for fast edge computation and privacy concerns have shifted researchers’ focus towards a type of distributed learning known as federated learning (FL). Recently, much research has been carried out on FL; however, a major challenge is the need to tackle the high diversity in different clients. Our research shows that using highly diverse data sets in FL can lead to low accuracy of some local models, which can be categorised as anomalous behaviour. In this paper, we present FedBranched, a clustering-based framework that uses probabilistic methods to create branches of clients and assigns their respective global models. Branching is performed using hidden Markov model clustering (HMM), and a round of branching depends on the diversity of the data. Clustering is performed on Euclidean distances of mean absolute percentage errors (MAPE) obtained from each client at the end of pre-defined communication rounds. The proposed framework was implemented on substation-level energy data with nine clients for short-term load forecasting using an artificial neural network (ANN). FedBranched took two clustering rounds and resulted in two different branches having individual global models. The results show a substantial increase in the average MAPE of all clients; the biggest improvement of 11.36% was observed in one client.


Optimization of different TDM techniques in DWDM optical networks for FWM suppression
Alishah Khalid, Habib Ullah Manzoor, Hafiz Ashiq Hussian, and Moustafa H. Aly
Springer Science and Business Media LLC

RECENT SCHOLAR PUBLICATIONS

Effect of Optimized Tilt Angle of PV Modules on Solar Irradiance for Residential and Commercial Buildings in Different Cities of Pakistan: Simulation‐Based Study
HU Manzoor, SM Aaqib, T Manzoor, F Azeem, MW Ashraf, S Manzoor
Energy Science & Engineering 2025



Exploring the Potential of Cross‐Border Energy Trade in SAARC Countries for Achieving Sustainable Development Goals (SDGs)
H Zidan, MT Malik, U Rafique, F Azeem, T Manzoor, HU Manzoor
Energy Science & Engineering 2025



Integrated Fuzzy‐Knapsack Based Demand Response Energy Management System for Smart Grid Buildings
Z Memon, F Azeem, T Manzoor, HU Manzoor
Energy Science & Engineering 13 (2), 862-877 2025



Investigation of Thermal Management Capacity of Casson Electrolytes in Porous Electrodes in Lithium‐Ion Battery Applications
T Manzoor, S Iqbal, T Anwer, S Manzoor, G Mustafa, HU Manzoor
Battery Energy, e20240082 2025



Optimization of indium concentration and compositional grading in InGaN heterojunction solar cells by SCAPS-1D simulation
MK Omar, HU Manzoor, SS Ng, MMM Rashid, MZ Pakhuruddin
Physica Scripta 100 (2), 025509 2025



Semantic-aware federated blockage prediction (sfbp) in vision-aided next-generation wireless network
AR Khan, HU Manzoor, RNB Rais, S Hussain, L Mohjazi, MA Imran, ...
IEEE Transactions on Network and Service Management 2025



Hybrid Neuromorphic‐Federated Learning for Activity Recognition Using Multi‐modal Wearable Sensors
AR Khan, HU Manzoor, F Ayaz, MA Imran, A Zoha
Multimodal Intelligent Sensing in Modern Applications, 133-164 2024



Robustness Against Data Integrity Attacks in Decentralized Federated Load Forecasting
A Shabbir, HU Manzoor, MN Manzoor, S Hussain, A Zoha
Electronics 13 (23), 4803 2024



Evaluation of the potential power generation resources in SAARC region for sustainable energy trade
U Rafique, HA Zidan, F Azeem, S Amir, T Manzoor, HU Manzoor
Energy Science & Engineering 12 (12), 5739-5752 2024



Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?
HU Manzoor, S Hussain, D Flynn, A Zoha
Energy and Buildings 324, 114871 2024



Adaptive single-layer aggregation framework for energy-efficient and privacy-preserving load forecasting in heterogeneous federated smart grids
HU Manzoor, A Jafri, A Zoha
Internet of Things 28, 101376 2024



Rethinking Federated Learning: An Adversarial Perspective on Global vs. Local Learning for Load Forecasting
HU Manzoor, A Shabiar, DC Nguyen, L Mohjazi, A Kaushik, A Zoha
2024 IEEE Conference on Standards for Communications and Networking (CSCN 2024



Enhancing consumer privacy in federated load forecasting through single layer aggregation
HU Manzoor, MA Jamshed, A Zoha, S Manzoor
IEEE Consumer Electronics Magazine 2024



A survey of security strategies in federated learning: Defending models, data, and privacy
HU Manzoor, A Shabbir, A Chen, D Flynn, A Zoha
Future Internet 16 (10), 374 2024



Computational Optimization for CdS/CIGS/GaAs Layered Solar Cell Architecture
S Bhatti, HU Manzoor, A Zoha, R Ghannam
Energies 17 (18), 4758 2024



Sustainable and lightweight defense framework for resource constraint federated learning assisted smart grids against adversarial attacks
A Shabbir, HU Manzoor, K Arshad, K Assaleh, Z Halim, A Zoha
Authorea Preprints 2024



Swarm‐Optimized ZnO/CdS/CIGS/GaAs Solar Cell for Enhanced Efficiency and Thermal Resilience
HU Manzoor, T Manzoor, S Hussain, MN Manzoor, A Zoha
Advanced Energy and Sustainability Research, 2400203 2024



Finite Element Approach for Rheological Behavior in Colloidal Electrolytes in Lithium-Ion Battery Performance
A Raza, T Manzoor, S Iqbal, T Anwar, A Ashraf, HU Manzoor
ACS omega 9 (33), 35809-35820 2024



Computational property optimisation for CIGS/CdS/GaAs layered solar cell architecture
S Bhatti, HU Manzoor, A Zoha, R Ghannam
Energies 2024



Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan
A Basit, HU Manzoor, M Akram, HE Gelani, S Hussain
The Journal of Engineering 2024 (7), e12405 2024

MOST CITED SCHOLAR PUBLICATIONS

Parametric analysis of four wave mixing in DWDM systems
J Ahmed, A Hussain, MY Siyal, H Manzoor, A Masood
Optik 125 (7), 1853-1859 2014
Citations: 36



Revolutionizing low‐cost solar cells with machine learning: a systematic review of optimization techniques
S Bhatti, HU Manzoor, B Michel, RS Bonilla, R Abrams, A Zoha, ...
Advanced Energy and Sustainability Research 4 (10), 2300004 2023
Citations: 31



Improving FWM efficiency in bi-directional ultra DWDM-PON networking centered light source by using PMD emulator
HU Manzoor, M Zafar, SU Manzoor, T Khan, S Liu, T Manzoor, S Saleem, ...
Results in Physics 16, 102922 2020
Citations: 30



High conversion and quantum efficiency indium-rich p-InGaN/p-InGaN/n-InGaN solar cell
HU Manzoor, MAM Zawawi, MZ Pakhuruddin, SS Ng, Z Hassan
Physica B: Condensed Matter 622, 413339 2021
Citations: 24



FWM mitigation in DWDM optical networks
HU Manzoor, T Manzoor, A Hussain, MH Aly
Journal of Physics: Conference Series 1447 (1), 012033 2020
Citations: 23



FedBranched: Leveraging Federated Learning for Anomaly-Aware Load Forecasting in Energy Networks
HU Manzoor, AR Khan, D Flynn, MM Alam, M Akram, MA Imran, A Zoha
Sensors 23 (7), 3570 2023
Citations: 20



FWM Reduction Using Different Modulation Techniques and Optical Filters in DWDM Optical Communication Systems: A Comparative Study
HU Manzoor, T Manzoor, A Hussain, MH Aly, S Manzoor
Iranian Journal of Science and Technology, Transactions of Electrical 2019
Citations: 19



A privacy and energy-aware federated framework for human activity recognition
AR Khan, HU Manzoor, F Ayaz, MA Imran, A Zoha
Sensors 23 (23), 9339 2023
Citations: 18



Defending federated learning from backdoor attacks: Anomaly-aware fedavg with layer-based aggregation
HU Manzoor, AR Khan, T Sher, W Ahmad, A Zoha
2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile 2023
Citations: 17



Fedclamp: An algorithm for identification of anomalous client in federated learning
HU Manzoor, MS Khan, AR Khan, F Ayaz, D Flynn, MA Imran, A Zoha
2022 29th IEEE International Conference on Electronics, Circuits and Systems 2022
Citations: 17



Reduction of four wave mixing by employing circular polarizers in DWDM optical networks
HU Manzoor, AU Salfi, T Mehmood, T Manzoor
2015 12th International Bhurban Conference on Applied Sciences and 2015
Citations: 17



Energy management in an agile workspace using ai-driven forecasting and anomaly detection
HU Manzoor, AR Khan, M Al-Quraan, L Mohjazi, A Taha, H Abbas, ...
2022 4th Global Power, Energy and Communication Conference (GPECOM), 644-649 2022
Citations: 15



Resilience of federated learning against false data injection attacks in energy forecasting
A Shabbir, HU Manzoor, RA Ahmed, Z Halim
2024 International Conference on Green Energy, Computing and Sustainable 2024
Citations: 14



A survey of security strategies in federated learning: Defending models, data, and privacy
HU Manzoor, A Shabbir, A Chen, D Flynn, A Zoha
Future Internet 16 (10), 374 2024
Citations: 12



Improved transmission length in the presences of ambient noise and scintillation effect using duobinary modulation in 40 Gbps free space optical channel
HU Manzoor, S Manzoor, T Manzoor, T Khan, A Hussain
Microwave and Optical Technology Letters 62 (10), 3163-3169 2020
Citations: 12



Numerical simulation of homojunction pin In0. 4Ga0. 6N solar cell with different absorber layer configurations
AK Tan, HU Manzoor, NA Hamzah, MA Ahmad, SS Ng, Z Hassan
Optik 271, 170095 2022
Citations: 11



Carrier Density and Thickness Optimization of Inx Ga1-xN Layer by Scaps-1D Simulation for High Efficiency III-V Solar Cell
HU MANZOOR, T KWAN, SS Ng, Z HASSAN
Sains Malaysiana 51 (5), 1567-1576 2022
Citations: 11



Performance Analysis of Magnetic Nanoparticles During Targeted Drug Delivery: Application of OHAM
M Zafar, MS Ullah, T Manzoor, M Ali, K Nazar, S Iqbal, HU Manzoor, ...
Computer Modeling in Engineering & Sciences 2021
Citations: 11



Adaptive single-layer aggregation framework for energy-efficient and privacy-preserving load forecasting in heterogeneous federated smart grids
HU Manzoor, A Jafri, A Zoha
Internet of Things 28, 101376 2024
Citations: 10



Enhanced adversarial attack resilience in energy networks through energy and privacy aware federated learning
HU Manzoor, K Arshad, K Assaleh, A Zoha
Authorea Preprints 2024
Citations: 10