Voltage and frequency deviation in the islanded operation of a microgrid (MG), due to the uncertainty and lack of inertia in the selection of optimal proportional integral (PI) controller gain, is a challenging task. Although various optimization algorithms have been proposed to achieve this task, most of them require a large number of iterations and are time intensive, making them inefficient for real-time applications. Gray wolf optimization (GWO), a new meta-heuristic algorithm, addresses these issues and has many advantages, including simplicity due to fewer control parameters, flexibility, and globalism. This paper proposes a simple and efficient modified algorithm, called square root gray wolf optimization (SRGWO) algorithm, to realize superior hunting performance. SRGWO is verified using twenty-three benchmark test functions. The algorithm is applied for optimal voltage and frequency regulation of a photovoltaic-based microgrid system operating in the islanded mode during distributed generation insertion and load change conditions. The voltage and frequency gain parameters of the PI controller are optimized. A comparison of the simulation results of the SRGWO algorithm with those of the original gray wolf algorithm (GWO), particle swarm optimization (PSO), augmented gray wolf optimization (AGWO), enhanced gray wolf optimization (EGWO), and gravitational search algorithm (GSA) reveal that the proposed SRGWO algorithm significantly improves system performance while maintaining its simplicity and easy implementation. Furthermore, the SRGWO algorithm obtains the minimum fitness function value in fewer iterations than other algorithms. Moreover, it improves the power quality of the system with regard to minimum total harmonic distortion.

This paper compares seven forecasting models for hourly electricity consumption in a commercial office building using data spanning 2024–2025. Models include XGBoost, LSTM, GRU, 1D‐CNN, SARIMA, Prophet, and Seasonal Naive baseline. Features encompass temporal indicators (hour, day of week, month), autoregressive lags (1, 2, 24, 168 h), and rolling statistics. Evaluation uses Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) on a 14‐day test set (336 samples) with rigorous hyperparameter tuning via GridSearchCV and TimeSeriesSplit cross‐validation. XGBoost achieves superior performance (MAE 6.29 kW, 3.5% MAPE) compared to GRU (10.95 kW), 1D‐CNN (11.86 kW), LSTM (14.98 kW), Seasonal Naive (16.15 kW), Prophet (35.72 kW), and SARIMA (48.16 kW). Paired t‐tests confirm statistical significance: XGBoost versus GRU () and versus Seasonal Naive (). Surprisingly, deep learning models underperformed gradient boosting despite theoretical sequence‐modeling advantages, attributed to modest sample size (17,016), rich feature engineering capturing 69.5% of variance through autoregressive features, and single‐hour forecasting horizon. Classical statistical models exhibited catastrophic failures, reflecting inadequate modeling of non‐stationary, non‐linear building consumption with multiple seasonal patterns. Results demonstrate that for structured tabular time series with comprehensive feature engineering, gradient boosting substantially outperforms sequential neural architectures and classical statistical methods. The findings enable high‐confidence building energy management decisions (HVAC pre‐conditioning, demand response) with ±6.3 kW prediction accuracy. Code and reproducibility documentation are available at https://github.com/SaadHayat91/BEJ-Electricity-Forecasting .

Determining the aging state of low-voltage nuclear power plant cables using a nondestructive and reliable condition monitoring technique is highly desirable as the cables experience multiple aging stresses during the service period. This paper deals with the implementation and investigation of such nondestructive techniques, which can detect the overall aging state of low-voltage instrumentation and control (I&C) cables, which are subjected to accelerated thermal aging. The dielectric spectroscopy, extended voltage response, and polarization–depolarization current as nondestructive electrical aging techniques were used for the investigation purpose, while the elongation at break was also adopted as a mechanical measurement and for comparison. Prominent variations in the electrical parameters for the insulation and jacket were observed, whereas the elongation at break for both materials also decreased under thermal aging. Based on the electrical techniques, aging markers were selected that showed a strong correlation with the aging and elongation at break, proving the ability of the adopted electrical methods as a nondestructive condition monitoring technique.