Recent achievements, based on lead (Pb) halide perovskites, have prompted comprehensive research on low-cost photovoltaics, in order to avoid the major challenges that arise in this respect: Stability and toxicity. In this study, device modelling of lead (Pb)-free perovskite solar cells has been carried out considering methyl ammonium tin bromide (CH₃NH₃SnBr₃) as perovskite absorber layer. The perovskite structure has been justified theoretically by Goldschmidt tolerance factor and the octahedral factor. Numerical modelling tools were used to investigate the effects of amphoteric defect and interface defect states on the photovoltaic parameters of CH₃NH₃SnBr₃-based perovskite solar cell. The study identifies the density of defect tolerance in the absorber layer, and that both the interfaces are 10¹⁵ cm⁻³, and 10¹⁴ cm⁻³, respectively. Furthermore, the simulation evaluates the influences of metal work function, uniform donor density in the electron transport layer and the impact of series resistance on the photovoltaic parameters of proposed n-TiO₂/i-CH₃NH₃SnBr₃/p-NiO solar cell. Considering all the optimization parameters, CH₃NH₃SnBr₃-based perovskite solar cell exhibits the highest efficiency of 21.66% with the V_oc of 0.80 V, J_sc of 31.88 mA/cm² and Fill Factor of 84.89%. These results divulge the development of environmentally friendly methyl ammonium tin bromide perovskite solar cell.

Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized.

A microgrid (MG) is a small-scale power system with a cluster of loads and distributed generators operating together through energy management software and devices that act as a single controllable entity with respect to the grid. MG has become a key research element in smart grid and distribution power systems. MG mainly contains different renewable energy sources (RESs) that use various technological advancements, such as power electronics-based technologies. However, it has an unstable output, thereby causing different types of power quality (PQ) events. As a result, standards and mitigation methods have been developed in recent years. To mitigate PQ issues due to MG integration, various methods and standards have been proposed over the last years. Although these individual methods are well documented, a comparative overview had not been introduced so far. Thus, this study aims to fill the gap by reviewing and comparing the prior-art PQ issues, solutions, and standards in MGs. We compare the main issues related to voltage sag, voltage swell, voltage and current harmonics, system unbalances, and fluctuations to ensure high-quality MG output power. The new technologies associated with MGs generate harmonics emission in the range of 2–150 kHz, thereby causing a new phenomenon, namely, supraharmonics (SH) emission, which is not sufficiently covered in the literature. Therefore, the characteristics, causes, consequences, and measurements of SH are highlighted and analyzed. The mitigation strategies, control, and devices of PQ issues are also discussed. Moreover, a comparison is conducted between the most popular devices used to mitigate the PQ issues in MG in terms of cost, rating, and different aspects of performance. This review study can strengthen the efforts toward the mitigation and standards development of PQ issues in MG applications, especially SH. Finally, some recommendations and suggestions to improve PQ of MG, including SH, are highlighted.

Solar is a significant renewable energy source. Solar energy can provide for the world’s energy needs while minimizing global warming from traditional sources. Forecasting the output of renewable energy has a considerable impact on decisions about the operation and management of power systems. It is crucial to accurately forecast the output of renewable energy sources in order to assure grid dependability and sustainability and to reduce the risk and expense of energy markets and systems. Recent advancements in long short-term memory (LSTM) have attracted researchers to the model, and its promising potential is reflected in the method’s richness and the growing number of papers about it. To facilitate further research and development in this area, this paper investigates LSTM models for forecasting solar energy by using time-series data. The paper is divided into two parts: (1) independent LSTM models and (2) hybrid models that incorporate LSTM as another type of technique. The Root mean square error (RMSE) and other error metrics are used as the representative evaluation metrics for comparing the accuracy of the selected methods. According to empirical studies, the two types of models (independent LSTM and hybrid) have distinct advantages and disadvantages depending on the scenario. For instance, LSTM outperforms the other standalone models, but hybrid models generally outperform standalone models despite their longer data training time requirement. The most notable discovery is the better suitability of LSTM as a predictive model to forecast the amount of solar radiation and photovoltaic power compared with other conventional machine learning methods.

With the rise in the demand for electric vehicles, the need for a reliable charging infrastructure increases to accommodate the rapid public adoption of this type of transportation. Simultaneously, local electricity grids are being under pressure and require support from naturally abundant and inexpensive alternative energy sources such as wind and solar. This is why the world has recently witnessed the emergence of renewable energy-based charging stations that have received great acclaim. In this paper, we review studies related to this type of alternative energy charging infrastructure. We provide comprehensive research covering essential aspects in this field, including resources, potentiality, planning, control, and pricing. The study also includes studying and clarifying challenges facing this type of electric charging station and proposing suitable solutions for those challenges. The paper aims to provide the reader with an overview of charging electric vehicles through renewable energy and establishing the ground for further research in this vital field.

Student performance is crucial to the success of tertiary institutions. Especially, academic achievement is one of the metrics used in rating top-quality universities. Despite the large volume of educational data, accurately predicting student performance becomes more challenging. The main reason for this is the limited research in various machine learning (ML) approaches. Accordingly, educators need to explore effective tools for modelling and assessing student performance while recognizing weaknesses to improve educational outcomes. The existing ML approaches and key features for predicting student performance were investigated in this work. Related studies published between 2015 and 2021 were identified through a systematic search of various online databases. Thirty-nine studies were selected and evaluated. The results showed that six ML models were mainly used: decision tree (DT), artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), linear regression (LinR), and Naive Bayes (NB). Our results also indicated that ANN outperformed other models and had higher accuracy levels. Furthermore, academic, demographic, internal assessment, and family/personal attributes were the most predominant input variables (e.g., predictive features) used for predicting student performance. Our analysis revealed an increasing number of research in this domain and a broad range of ML algorithms applied. At the same time, the extant body of evidence suggested that ML can be beneficial in identifying and improving various academic performance areas.