Partially and randomly based charging information on electric vehicles and energy storage systems is essential in the proper selection of charging strategies to guarantee effectiveness and safety, and this depends heavily on the accurate online estimation of battery state of health$(\text{SOH})$. This paper presents a comprehensive end-to-end evaluation protocol that uniformly samples a variety of different aspects of partial charging and a comparison of three kinds of neural network architecture feedforward (FNN), convolutional (CNN), and long short-term memory (LSTM)) under direct and transfer learning conditions. The latency and error rates of inference on each of these models are profiled to determine viability to be deployed on-board. Explainability methods detect key charging intervals that work on the predictions. Observations demonstrate that rather than just lightweight implementation, FNN with moderate accuracy, when compared to CNN, leads to enhanced performance through the extraction of local patterns and the best precision when compared to LSTM which models sequences. All the architectures are always improved with transfer learning, with average errors roughly decreasing by$\mathbf{0. 0 5}-\mathbf{0. 1 \%}$point and error bands getting narrower. As far as model compression is concerned, it is posited that the application of efficient LSTM variants would accommodate embedded hardware constraints. The development of this work will offer usable information on how to pick and tune deep learning models to perform well, maintain real-time SOH monitoring in realistic charging situations.

The growth of the renewable energy target has increased the need for a powerful solar system. However, those caused by solar power production itself from irradiation, temperature, and the state of the solar panels present tough challenges in prediction, beneficial use and optimization. This study aims to accurately model solar power generation forecasting using machine learning models like Linear Regression, Decision Tree Regressor and Random Forest Regressor. The study investigates the influence of the environment on power output, underlining the daily diurnal variability of irradiation and temperature. Here, we describe how time-series modelling and clustering algorithms help identify patterns of solar power behavior distilled from data collected across multiple months and locations. The results show that compared to other models, the Random Forest model boosted forecasting accuracy by capturing non-linear overheads and minimizing the variance. The best-case scenario yields 92.5 W peak DC power generation, and the worstcase scenario shows 12 W minimum peak DC power generation. Moreover, solar power generation is heavily affected by temperature and irradiation changes, as in daily temperature fluctuations from 2 0° C to 8 5° C, in addition to combining anomaly detection using Z-Score analysis and Isolation Forest algorithms to identify problems such as degradation of panels and fault of sensors. These findings emphasize the necessity for a near realtime model and complex forecasting models to mitigate issues associated with renewable energy precursors to increase solar energy productivity and efficiency.

The accurate estimation of the state of charge (SOC) in batteries is a critical component of battery management systems (BMS), especially in electric vehicles (EVs), where it directly impacts the efficiency, longevity, and safety of the system. This paper investigates two widely used SOC estimation techniques: the Coulomb counting method and the Extended Kalman filter (EKF) algorithm. The Coulomb counting (CC) method estimates SOC by integrating the battery current over time, making it simple and computationally efficient. However, it suffers from errors due to inaccuracies in the initial SOC estimate and does not account for battery self-discharge. In contrast, the Kalman filter algorithm is a dynamic estimation technique that uses a probabilistic model to estimate SOC, providing more accurate results even in noisy measurements and initial errors. This study compares both techniques by evaluating their performance in terms of accuracy, adaptability, and computational complexity in various battery usage scenarios. The Coulomb counting method, starting with an initial SOC estimate of 80%, shows a maximum estimation error of 15% over a sixhour charge-discharge cycle. The Kalman filter, initialized with a SOC of$\mathbf{8 0 \%}$, converges to the real SOC value of$\mathbf{5 0 \%}$within$\mathbf{1 0}$minutes, achieving an estimation error of less than 5%. The results show that while Coulomb counting is effective in short-term applications with accurate initial estimates, the Kalman filter excels in long-term SOC estimation, offering superior performance in dynamic and noisy conditions. The paper concludes by discussing the practical applications of both methods and providing recommendations for choosing the appropriate technique based on specific system requirements.

Smart grids are under pressure due to rising energy use, more renewable sources, and unpredictable consumption. When control systems fail to respond in time, they can cause frequency changes, power imbalances, and reduced grid reliability. This paper introduces a reinforcement learning (RL) framework using Q-learning to handle these challenges through real-time, adaptive control. The approach uses Principal Component Analysis (PCA) to reduce data complexity and discretizes continuous variables to make learning more efficient. A custom Markov Decision Process (MDP) models the grid environment, where the agent chooses actions: Increase, Decrease, or Hold based on the current state. A tabular Q-learning algorithm helps the agent learn the best decisions by maximizing rewards over time. Results show that the RL agent improves power stability by 22% over baseline methods and reacts accurately to supply and demand shifts, with action preferences distributed as Increase (58%), Hold (31%), and Decrease (11%). Heatmaps and 3D plots reveal clear action patterns and strong confidence in decisions, with more than 85% of states showing a decisive optimal action. The model adapts well to changes, proving useful for intelligent and stable grid control. This work supports smarter energy systems.

Phishing attacks are a major cybersecurity concern. These attacks continue to grow in complexity and often bypass traditional detection systems by imitating legitimate communication payloads. Many existing models, especially classical machine learning techniques, lack the ability to detect hidden or adversarial phishing payloads. They also offer limited transparency in their predictions. This research presents a phishing payload detection approach using a fine-tuned DistilBERT model. The methodology includes dataset preprocessing, model fine-tuning, adversarial training, explainability analysis, and performance evaluation. DistilBERT, a lightweight transformer model, is finetuned to detect phishing payloads with improved accuracy and robustness. Adversarial training is applied to defend against input manipulation. Explainable AI (XAI) techniques such as LIME and SHAP are used to interpret the model's predictions. This research shows that DistilBERT achieves a classification accuracy of 98.52% and an AUC score of 0.9993, outperforming traditional machine learning models. It also maintains low false positives and high recall. This research improves the reliability of phishing detection and provides interpretable outputs for security analysis. The results demonstrate that the proposed framework strengthens phishing detection strategies and increases resilience to adversarial attacks. The results are based on a single publicly available phishing email dataset and further validation across diverse datasets and real-world environments is required, with the scope of the findings limited to email-based phishing detection.

This paper aims at examining how machine-learning can be applied to achieve optimal wind-solar hybrid towers performance in relation to predicting thermal updraft. Wind-solar towers use solar irradiance and wind speed to generate energy but due to the complex nature of interaction between the various environment variables, wind-solar towers are relatively hard to predict the thermal updraft due to the interaction between the solar irradiance, air temperature, and wind velocity. A complete dataset that covers 90 days is used, and various machine-learning models, such as linear regression, support vector machines, and neural networks, are tested to make a prediction of thermal updraft. The findings show that among all the alternative models, linear regression had a much superior predictability and efficiency in computation. The obtained result showed a close-toperfect fit,$\mathrm{R}^{2}$being equal to 0.98 and indicating the extremely high ability of the regression model to make predictions of the updraft velocity under heterogeneous environmental conditions. Sensitivity analysis had demonstrated that solar irradiance and wind speed are the best predictors of thermal updraft thereby offering insights that would be adopted to streamline the hybrid renewable energy system, and energy forecasting with potential to boost power generation especially during days with low solar irradiance.

Quantum computing faces challenges such as noise, short coherence time, and limited qubit connections. These challenges worsen as quantum circuits become more complex. One major issue is the increasing depth of quantum circuits. This research proposes an optimization framework targeting depth and gate count reduction in quantum circuits, specifically for Noisy Intermediate-Scale Quantum (NISQ) devices. The proposed approach combines unitary merging of single-qubit gates, CNOT cancellation, gate commuting, and rotation gate rewriting strategies. Consecutive gates acting on the same qubit, such as$R_{x}\left(\theta_{1}\right) \cdot R_{x}\left(\theta_{2}\right)$, are algebraically merged into a single rotation, while pairs of redundant CNOT gates are eliminated based on gate identity relations. The technique is implemented using Qiskit and validated across five diverse circuits including complex, random, and multi-qubit configurations. Experimental results show an average depth reduction of 33.33% and gate count reduction of 32.14%, with runtime improvement of up to 25%. For instance, an input circuit with a depth of 7 and gate count of 11 was reduced to a depth of 2 and 4 gates. All optimized circuits preserve functional correctness with a fidelity$F \geq 0.99$. High-resolution circuit diagrams are presented to visually demonstrate improvements before and after optimization. Additionally, global phase shifts such as$e^{i \pi / 4}$are preserved or analytically characterized where relevant. This work enhances the viability of quantum computations on near-term hardware and opens pathways for future AI-driven quantum optimizations.

This study investigates the distribution of electricity demand is distributed over temperature, humidity, wind speed and seasonal variations. Using data covering the timeframe of five years (2021-2025), the objective behind this research is to understand the interdependent nature of the electric demand and different external conditions of the environment. Preliminary analysis indicates that the demand for electricity varies based on the time of the day, the season and the day of the week. Peak demand is at noon in winter at 27,000 units and declines to 22,000 units during post-midnight. Electric demand positively correlated with temperature and humidity, where a notable increase in demand was detected when temperature increased from 5°C to 8°C and when humidity increased from 60% to 66% The wind speed variation of the demand shows that the demand is less than$2 ~\mathrm{m} / \mathrm{s}$and higher than$5 ~\mathrm{m} / \mathrm{s}$. The impact of the seasonal change is also studied in this paper and the results show that the demand is higher in the winter and summer due to the heating and cooling need respectively while the demand is lower in the spring and autumn. The findings deliver specific intelligence on optimizing how energy management strategies can best be employed, specifically for ensuring that demand drops are anticipated ahead of time around peak seasons and extreme weather.

The paper proposes a combined approach of Solar and wind power fluctuation equalization through Electrochemical Ammonia Synthesis (EAS) and Direct Ammonia Fuel Cells (DAFCs), supplemented by different smoothing filters. When full of renewable energy, the surplus is turned into ammonia and stored and later turned back into electricity when there is a shortage. It uses Moving Average, Moving Median, Moving Regression, Gaussian and Savitzky Golay filters on real wind and solar profiles in MATLAB and uses EAS/DAFC models in Engineering Equation Solver (EES). Findings indicate that the MR filter provides the best compromise between noise attenuation and trend fidelity, and peak capacity demands of ammonia production and fuel cell output are lowered by approximately$\mathbf{1 0 - 2 0 \%}$relative to raw profiles. SG and Gaussian filters have nearly similar advantages, whereas MA and MM are suboptimal because of either lag or inconsistent trend. This is the novelty of the work because a comparative evaluation of advanced smoothing methods in an ammonia-based storage cycle has never been carried out, and results are used to inform actionable recommendations regarding storage cycle sizing and cost minimization. The suggested methodology increases the stability of the grid, reduces the sizing of subsystems, and enables the cost-efficient implementation of ammonia fuel cells to integrate renewable energies.

The transition to Electric Vehicles (EVs) is crucial for sustainable transportation. However, the adoption of traditional plug-in charging systems remains limited due to their time-consuming nature and infrastructure demands. This study presents a Wireless Power Transfer (WPT) system leveraging inductive coupling to enhance EV charging efficiency, compatibility, and safety. The system converts AC to DC with power factor correction, then transforms it into high-frequency AC to energize the transmitter coil. The receiver coil generates alternating current (AC), which then converts to direct current (DC) for charging the battery. The proposed WPT model, developed using MATLAB Simulink Optimizer, demonstrates a charging efficiency ranging from 90 % to 93 %, resulting in energy losses as low as 10 % to 15 % and improved user convenience by reducing the need for manual plug-in operations. The developed model assumes a standard 60 kWh EV battery can be charged from 20 % to 80 % in approximately 3 hours using the proposed WPT system, compared to 5–6 hours with conventional plug-in chargers.