Expeditious urbanization and rapid industrialization have significantly influenced the rise of energy demand globally in the past two decades. Solar energy is considered a vital energy source that addresses this demand in a cost-effective and environmentally friendly manner. Improving solar cell efficiency is considered a prerequisite to reinforcing silicon solar cells’ growth in the energy market. In this study, the influence of various parameters like the thickness of the absorber or wafer, doping concentration, bulk resistivity, lifetime, and doping levels of the emitter and back surface field, along with the surface recombination velocity (front and back) on solar cell efficiency was investigated using PC1D simulation software. Inferences from the results indicated that the bulk resistivity of 1 $\mathsf{\Omega }$·cm; bulk lifetime of 2 ms; emitter ($n^{+}$) doping concentration of $1\times {10}^{20}$ cm${}^{-3}$ and shallow back surface field doping concentration of $1\times {10}^{18}$ cm${}^{-3}$; surface recombination velocity maintained in the range of ${10}^2$ and ${10}^3$ cm/s obtained a solar cell efficiency of 19%. The Simulation study presented in this article allows faster, simpler, and easier impact analysis of the design considerations on the Si solar cell wafer fabrications with increased performance.

The transition from conventional to electric transportation has become inevitable in recent years owing to the significant impact of electric vehicles (EVs) on energy sustainability, reduction of global warming and carbon emission reduction. Despite the rapidly growing global adoption of EVs in today’s electrical and transportation networks, energy storage in EVs, particularly in regards to bulky size and charging process, still remains a major bottleneck. As a result, wireless charging of EVs via inductively coupled power transfer (ICPT) through coupled coils is becoming a promising solution. However, the efficiency of charging EV batteries via wireless charging is hugely affected by misalignment between the primary and secondary coils. This paper presents an in-depth analysis of various key factors affecting the efficiency of EV battery charging. Finite element analysis (FEA) using Ansys Maxwell^® is performed on commonly used coil designs such as circular and rectangular coils under various misalignment conditions. In addition, various reactive power compensation topologies applied in ICPT are investigated and the behavior of each topology is observed in simulation. It is revealed that circular structures with S–S compensation topology show more robustness in misalignment conditions and maintain the desired efficiency for a wider range of displacement. A critical analysis of coil designs, compensation techniques and the combination of both factors is accomplished and conclusions are presented.

This paper presents a new optimization technique for the locations and sizes of Distributed Generators (DGs) and distribution static compensators (DSTATCOMs) in a radial system of a distribution network based on a multi-objective approach. It uses black widow optimization to improve voltage profile and power loss reduction. The black widow optimization simulates the mating behaviour of black widow spiders. The optimum size and placement of DGs and DSTATCOMs are deemed to be decision variables that are defined by using black widow optimization. The proposed technique is implemented in selected IEEE bus systems to evaluate its performance. The simulation results indicate reduced power losses and voltage profile enhancement as sizes and locations of integrated DGs and DSTATCOMs are adjusted based on optimization. The number of DGs and DSTATCOMs required to achieve the objectives is reduced. Furthermore, the results of the black widow algorithm are compared to existing techniques in the literature.

Heating, ventilation, and air conditioning (HVAC) systems account for 60% of the energy consumption in commercial buildings. Each year, millions of dollars are spent on electricity bills by commercial building operators. To address this energy consumption challenge, a predictive model named Bayesian optimisation Convolution Neural Network Multivariate Long Short-term Memory (BO CNN-M-LSTM) is introduced in this research. The proposed model is designed to perform load forecasting, optimizing energy usage in commercial buildings. The CNN block extracts local features, whereas the M-LSTM captures temporal dependencies. The hyperparameter fine tuning framework applied Bayesian optimization to enhance output prediction by modifying model properties with data characteristics. Moreover, to improve occupant well-being in commercial buildings, the thermal comfort adaptive model developed by de Dear and Brager was applied to ambient temperature in the preprocessing stage. As a result, across all four datasets, the BO CNN-M-LSTM consistently outperformed other models, achieving an 8% improvement in mean percentage absolute error (MAPE), 2% in normalized root mean square error (NRMSE), and 2% in R² score.This indicates the consistent performance of BO CNN-M-LSTM under varying environmental factors, highlight the model robustness and adaptability. Hence, the BO CNN-M-LSTM model is a highly effective predictive load forecasting tool for commercial building HVAC systems.

This study presents a novel LSTM–CNN-based adaptive scheduling framework (LSTM-CNN–AS) designed to improve real-time energy management and extend the lifespan of lithium-ion Battery Energy Storage Systems (BESS) in rural and resource-constrained microgrids. In contrast to conventional methods that prioritize economic optimization, the proposed framework incorporates state of health (SOH) aware control and adaptive closed-loop scheduling to enhance operational reliability and battery longevity. The architecture combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for accurate SOH estimation, with lightweight Multi-Layer Perceptron (MLP) models supporting real-time scheduling and state of charge (SOC) regulation. Operational safety is maintained by keeping SOC within 20–80% and SOH above 70%. The proposed model training and validation are conducted using two real-world datasets: the Mendeley Lithium-Ion SOH Test Dataset and the DKA Solar System Dataset from Alice Springs, both sampled at 5-min intervals. Performance is evaluated across three operational scenarios, which are 2C charging with random discharge; random charging with 3C discharge; and fully random profiles, achieving up to 44% reduction in MAE and an R²; score of 0.9767. A one-month deployment demonstrates a 30% reduction in charging time and 40% lower operational costs, confirming the framework’s effectiveness and scalability for rural microgrid applications.