Electricity theft is a serious issue that many nations face, especially in developing areas where non-technical losses can make up a significant percentage of the overall losses sustained by utilities. Electricity theft detection (ETD) is a very challenging task because it frequently introduces irregularities in customer electricity consumption patterns. In recent times, machine learning (ML) techniques have been investigated as a potential solution for ETD. In this research, author propose electricity theft detection based on four kernel functions of support vector machines (SVM). The proposed method analyzes the electricity consumption patterns and then predicts the category of the user. The kernel functions utilized includes polynomial, sigmoid, radial basis function (RBF) and linear kernel function. For experimentation and model training, a dataset of Pakistani utility company is used, which contains the electricity consumption information. The results highlight SVM method works well for accurate ETD. The detection accuracy of the various kernel functions of SVM is 83%, 79%, 80%, and 76% for RBF, polynomial, sigmoid, and linear kernel functions, respectively, demonstrating the effectiveness of the proposed SVM-based method for theft detection. By leveraging these ML-based methods, utility companies can strengthen their ability to detect and prevent electricity theft, leading to improved revenue management and dependability of services.

The growing adoption of flexible and portable electronic devices has created an increasing need for sustainable power sources that can operate without relying on conventional batteries. Triboelectric nanogenerators (TENGs) have attracted significant attention due to their ability to convert ambient mechanical energy into electrical energy. However, practical contact–separation TENGs may require additional spacers, elastic supports, or other structural components to maintain repeated contact and separation. This study presents a spacer-free arch-shaped TENG based on a laminated structure consisting of polytetrafluoroethylene (PTFE), paper, and aluminum. The arch configuration provides an inherent restoring tendency that facilitates contact–separation operation without a discrete spacer or additional elastic support. The fabricated device was experimentally evaluated through open-circuit voltage, short-circuit current, load-dependent electrical characterization, power measurement, capacitor charging, and LED illumination. Under the manual mechanical actuation conditions used in the present study, the device exhibited a maximum open-circuit voltage of 69 V, a reported peak short-circuit current of 68 $\mathsf{\mu }$A, and a peak power output of 8.576 $\mathsf{\mu }$W at an optimal load resistance of approximately 55 M$\Omega$. Furthermore, the TENG charged a 4.7 µF capacitor to 7 V within approximately 60 s and directly illuminated 30 commercial light-emitting diodes (LEDs). These results demonstrate the feasibility of combining a simple spacer-free arch-shaped configuration with readily available materials for low-cost triboelectric energy harvesting. Long-term cyclic durability, controlled mechanical excitation, environmental stability, and matched structural control experiments remain important areas for future investigation.

This study presents a robust, end-to-end machine learning framework for detecting Non-Technical Losses (NTLs) in smart power grids. The proposed Optimized Bootstrapped Gradient Classifier (OBGC) integrates k-Nearest Neighbor (kNN) imputation for missing data handling, Adaptive Synthetic Sampling (ADASYN) for class imbalance correction and Optuna-based hyperparameter optimization. The framework is evaluated using the State Grid Corporation of China (SGCC) dataset comprising 42,373 consumers over 1035 days (January 2014–October 2016), characterized by 25.6% missing values and a severe class imbalance (91.46% honest vs. 8.54% theft-prone consumers). To ensure robust evaluation, all experiments employ stratified 5-fold cross-validation on the training set, with a held-out 20% test set reserved for final performance assessment. Experimental results show that OBGC achieves an accuracy of 96.61%, precision of 96.18%, recall of 97.13%, and F1-score of 96.65%, substantially outperforming baseline XGBoost (accuracy 92.75%, F1 36.11%) and other state-of-the-art classifiers. An ablation study confirms that ADASYN is the most critical component; its removal causes recall to collapse from 97.13% to 16.87%, demonstrating the severe impact of class imbalance on theft detection. Optuna-based tuning contributes an additional 3.29 percentage-point gain in accuracy over the unoptimized baseline. The framework offers a transparent, reproducible, and operationally feasible solution for real-world NTL detection.

Hydrogen (H ₂ ) has attracted considerable attention as a promising energy carrier due to its high energy content compared to conventional fossil fuels. The continuous growth of the global population is driving an increasing demand for energy, leading to higher consumption of fossil fuels and, consequently, increased greenhouse gas emissions and climate change. This has driven the research community toward renewable energy sources (RESs) such as solar, wind, and tidal energy. Among RES options, hydrogen stands out as one of the most viable alternatives. The significant research has focused on hydrogen production and its applications, comparatively less attention has been given to hydrogen storage. In this context, the hydrogen storage characteristics of alkali metal (AM) and alkaline earth metal (AEM) decorated and defective hexagonal boron nitride (h‐BN) nanosheets were systematically investigated using density functional theory (DFT) calculations. The AM atom (Li) and AEM atom (Be) are found to be uniformly dispersed on h‐BN surfaces containing boron vacancies and divacancy defects, effectively preventing atom clustering. Up to 19 H ₂ molecules can be adsorbed on the surface of Be‐decorated Li‐doped h‐BN, exhibiting an average adsorption energy ranging from −0.23 to −0.42 eV per H ₂ molecule. Electronic structure analysis reveals that the introduction of Li and Be atoms significantly alters the electronic properties of pristine h‐BN, as indicated by the modification of bands near the Fermi level, thereby enhancing conductivity and inducing semiconducting behavior. Furthermore, Be decoration Li doping collectively improves the hydrogen storage capacity (HSC), achieving a theoretical gravimetric density of up to 11.22 wt.%. The thermal stability of the H ₂ ‐adsorbed system was further evaluated using ab initio molecular dynamics (AIMD) simulations and the van’t Hoff equation, confirming its robustness under thermal conditions.

Today’s distribution networks face challenging voltage stability problems from the increasing deployment of electric vehicles (EVs) and solar photovoltaic (PV) systems. The current methods of voltage stability assessment rely on computation-intensive power flow analysis, thereby limiting their applicability for real-time decision-making. This paper introduces a data-driven machine-learning-based approach to rapidly and interpretatively assess voltage security when considering EV and solar variability. The Voltage Security Score (VSS) is a continuous measure of security based on the power flow results of the Newton–Raphson calculation. The surrogate machine learning model is then trained only with actionable input variables including EV demand, solar generation and temporal variables to avoid leaking information from internal system states. The proposed model has good predictive accuracy, which can well reflect the nonlinear relationship between the operating conditions and the voltage stability. The framework supports VSS-based security-region screening, visualization of the operating envelope, and preliminary estimation of EV loading levels under different solar-generation conditions. The results show that the higher the level of solar generation, the greater the voltage stability and the higher the EV penetration. It provides a novel and efficient solution that is relevant from a practical perspective to plan and manage modern power system networks that include significant amounts of EVs and renewables.