Optimizing solar cells design is vital for boosting efficiency and cutting production costs, 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 paper 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/cm2 . Optimization also notices a significant improvement in quantum efficiency. The cells are tested under concentrated solar irradiance (1000 to 10000 W/m2 ) and temperatures (300 K to 800 K). Results show that at 10000 W/m2 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.

Federated Learning (FL) in load forecasting improves predictive accuracy by leveraging data from distributed load networks while preserving data privacy. However, the heterogeneous nature of smart grid load forecasting poses significant challenges to conventional FL methods, which are also unsuitable for resource-constrained devices. To address these issues, we propose a novel Lightweight Single Layer Aggregation (LSLA) framework tailored for smart grid networks. The LSLA framework mitigates the issue of data heterogeneity in load forecasting by emphasizing local learning and partially using updates from local devices for model aggregation. Additionally, the framework is optimized for resource-constrained devices by introducing a stopping criterion during model training and weight quantization. Our results show that after quantization, there is an acceptable degradation of 0.01% in Mean Absolute Error (MAE). Compared to traditional FL, up to a 1000fold reduction in communication overhead is achieved with LSLA. Moreover, with an 8-bit fixed-point data representation of neural network weights, a 75% reduction in storage/memory requirement is achieved, and computational cost is reduced due to the replacement of complex floating-point computational units with simpler fixed-point counterparts. By addressing data heterogeneity and minimizing data storage, computation, and communication overheads, our novel framework is well-suited for resource-constrained devices in smart grid networks.

The integration of artificial intelligence (AI) into energy networks significantly advanced short-term forecasting, particularly in smart meter applications. However, as distributed energy resources proliferated and energy systems grew in complexity, traditional centralized approaches to data analysis became insufficient in addressing privacy-preserving challenges. Federated learning (FL) emerged as a promising solution, leveraging distributed data sources while safeguarding user privacy. Nonetheless, FL encountered inherent vulnerabilities to adversarial attacks during model training, undermining its reliability and effectiveness. Existing techniques to eliminate these attacks often required additional frameworks for detection, imposing an added burden on devices. To address this issue, we proposed a novel method called federated random layer aggregation (FedRLA). It aggregated only one randomly chosen neural network layer on the server in a privacy-aware manner, leaving the remaining layers unchanged. FedRLA exhibited superior resilience against adversarial attacks by confining attackers to a single neural network layer. Our simulations, focusing on household energy consumption, demonstrated that FedRLA achieved 3.56 times less data transmission compared to FedAvg during global model training. This enhanced efficiency translated to improved energy usage and resource conservation. Furthermore, FedRLA performed better in the presence of differential privacy under attack and no attack conditions.

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.

The integration of AI and ML in energy forecasting is pivotal for modern energy management. Federated Learning (FL) stands out by enhancing data privacy and collaboration among distributed energy resources, enabling distributed model training while reducing reliance on centralized servers and data transfers. Despite its merits, FL faces substantial security challenges, particularly from adversarial attacks that can compromise the integrity and reliability of the models. This paper aims to address these security concerns by examining the efficiency of Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL) for distributed load forecasting. Through comparative analysis utilizing publicly available household datasets for short-term load forecasting, our study reveals that DFL demonstrates superior resilience against adversarial attacks compared to CFL. Notably, our findings indicate that the impact of adversarial model poisoning attacks is confined to the targeted client in DFL, while CFL exhibits broader susceptibility across all clients. When attacked, CFL's averaged client Mean Absolute Error (MAE) increased from 0.076 to 0.22 kWh, whereas DFL maintained a lower MAE of 0.116 kWh. Additionally, we present Decentralized Random Layer Aggregation (DRLA) to augment DFL's robustness, offering further insights into enhancing FL methodologies within energy contexts.

A document by Habib Ullah Manzoor. Click on the document to view its contents.