Abid Ali

The rapid growth of intelligent sensor-based consumer applications has significantly increased the need for an accurate, reliable, and trustworthy load forecasting framework. These frameworks play an important role in addressing key challenges in real-time resource allocation, operational planning, and the control of complex industrial networks, particularly through the integration of sensing, computing, and artificial intelligence. To mitigate these challenges, this paper presents an AI-assisted framework based on the hybrid combination of i) variational mode decomposition to decompose complex and non-stationary load patterns into different intrinsic modes to improve the interpretability, ii) autoencoder to enable the extraction of salient latent feature representation and noise reduction from the decomposed signals, and iii) the bidirectional long-short term memory network to effectively capture the complex spatial and temporal dependencies in both forward and backward directions for accurate and efficient load forecasting. The robustness of the proposed frameworks is evaluated using publicly available power consumption datasets. The extensive experimental results demonstrate that the proposed model outperforms other baseline models, with overall improvements of 18.54%~81.95% in mean absolute error (MAE), 21.72%~82.51% in root mean square error (RMSE), and 17.03%~82.11% in mean absolute percentage error (MAPE). The proposed model is also compared with state-of-the-art methods and it shows the overall improvement of 8.67%~72.85% in RMSE, 3.83%~71.30% in MAE, and 2.59%~69.79% in MAPE. The results validate that the proposed model provides an effective, efficient, reliable, and robust predictive solution for intelligent management and optimization in consumer energy applications.

Rapid advancements in urbanization and industrialization have increased consumer-end applications in smart grids, where advanced data-driven methods have significantly improved consumer-level energy management using smart meters. However, these systems are highly vulnerable to cyberattacks, notably false data injection attacks (FDIA), which can compromise grid reliability and stability. Existing detection techniques often rely on centralized data collection and processing, which raises substantial privacy concerns and leads to overfitting due to limited data diversity. To address these issues, this paper presents a novel framework that integrates federated learning with an autoencoder-based 1D deep convolutional neural network (AE-1DdeepCNN) for FDIA in consumer applications. The proposed framework enables collaborative model training based on the IEEE-14 bus system dataset for anomaly detection while ensuring data privacy. Extensive experimental results with row-accuracy of 93.35%~99.40% in different smart meters demonstrate the effectiveness and robustness of the proposed method for FDIA detection in smart grid systems and consumer smart metering applications.

Increasing urbanization and the global transition toward sustainable, eco-friendly energy systems require efficient and robust energy predictions for smart grids. The inherently unpredictable, volatile, and intermittent nature of energy demand necessitates an accurate short-term load forecasting model to ensure reliable consumer applications. However, conventional deep learning models often struggle to address complex and dynamic load patterns. To address these challenges, this research presents a novel trustworthy GAI-assisted model comprising i) a variational autoencoder that maps raw energy consumption data to extract meaningful and compact features and ii) a deep learning model utilizing a dual attention mechanism with convolutional long short-term memory (DAConvLSTM), that effectively captures the temporal dependencies of the complex load pattern and optimizes forecasting accuracy. The effectiveness and robustness of the proposed model are extensively evaluated using publicly available comprehensive datasets. The results demonstrate the performance of the proposed model, with an overall improvement of 1.45%81.54% in the mean absolute error, 1.92%78.61% in the root mean square error, and 1.55%81.85% in the mean absolute percentage error compared with other baseline methods. The results validate the effectiveness of the proposed model in predicting peak load demand and have practical implications, thereby enhancing the existing knowledge for creating robust energy management in smart grid applications.

Ever-increasing dynamic surges in renewable-based electric power systems, notably wind and photovoltaic farms bring adverse impacts and challenges in terms of reliability and stability. The intermittency of renewable sources imposes significant deviations in frequency due to variations in demand. Wind power induces instability in the grid due to its vulnerable nature, and reduction in system inertia. To mitigate these dynamics issues, an optimal control technique based on flatness-based Active disturbance rejection control (FADRC) and utilizing an enhanced Beluga Whale optimization algorithm (EBWO) for a multi-area interconnected power system with photovoltaic generation. The proposed LFC model addresses the load perturbation and the deviation of tie-line power, with system uncertainties considered as lumped disturbances that are approximated by extended state observers. To achieve optimal performance, the Enhanced Beluga Whale optimization algorithm is adopted and integrated with the suggested controller to fine-tune the controller. To validate the formidable performance of the suggested scheme, different cases have been studied with the existing approaches. The simulation results reveal the supremacy and robustness of the dynamic response of the Flatness-based active disturbance rejection control as compared to other approaches under load variations and parameter uncertainty.

Multi-source electrical systems rely on performing energy management to have a minimal operational cost while satisfying the energy demand. In this paper, an existing hybrid nano-grid composed of PV panels, a diesel generator, and batteries is used for the investigation purpose. The behavior of the actual electrical system is studied through its equivalent model developed in MATLAB/Simulink®. Recently, we noticed space for improvement in the control settings of the multi-source system. To further confirm the impact of these input set points on the usage of the diesel engine, a sensitivity analysis is also performed. An optimization based on a genetic algorithm demonstrates that changing these control set points through the different periods of the year could improve diesel engine usage and help to penetrate more renewable energy from PV modules.

The transformer is one of the most discussed and important components of electrical power systems because of its reliability, durability and energy conversion capability. It is also useful in load sharing, which reduces system burden, but is also responsible for a sufficient number of losses, as it is used in different types of electric appliances that require voltage conversion. The no-load losses of transformers have gained much attention from research perspective because of its operating cost throughout its lifetime. Many studies were carried out to achieve the highest possible efficiency, decreasing certain losses by using different methods and materials. However, the local market in Pakistan is far behind in the field of efficient core material manufacturing of transformers, which is why consumers are unable to obtain efficient electric appliances. Due to these loss-making appliances, the overall residential load increases and the consumers are charged with heavy electricity bills. This proposed study discusses core losses, different core comparisons, T/F efficiency and advancement in the core material. To accomplish a core comparison, two locally available core materials are used to fabricate two different T/F, and some tests such as open-circuit and short-circuit tests are performed to discover their losses, thermal degradation, and output efficiencies.