Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application’s ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers.

A lot of information is spread by people in the social media to update their status and share crucial news with others. But the majority of these platforms don’t promptly validate the individuals or their posts and people aren’t able to identify the fake news manually. Therefore, there is a need for an automated system capable of detecting fake news. This research has proposed to build a model using four machine learning algorithms. The dataset employed in the experiment is a composite of two datasets containing almost equal amounts of true and fake news articles on politics. The preprocessing stages begin with cleaning the data by removing punctuation, tokenization, special characters, white spaces, redundant word elimination, numerals, and English letters followed by stemming and stop with data discretization. Then, we analyzed the collected data and 80% of the data has been used to train each model initially. After that, the four manifested classification algorithms are applied. For identifying fake news from news articles, meth-ods like Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Classifier were used. The trained classifiers’ accuracy has been evaluated using the remaining 20% of the data. The results show that the decision tree model produces the best accuracy of 99.60% and gradient boosting of 99.55%. Besides, the random forest shows 99.10% along with the logistic regression 98.99%. Moreover, we have explored the best model to achieve the highest precision, recall, F1-score based on the confusion matrix’s outcome.

Hybrid PVT systems simultaneously produce electrical energy using photovoltaic technology and thermal energy using a heat extraction method that collects induced heat from the module. The purpose of this work is to establish a PVT system based on characterization, efficiency study, and performance analysis for both an electrical and a thermal system. A mathematical analysis of the electrical, thermal, and optical model is performed to establish the proposed system. Three types of heat exchanger pipes, including stainless steel, aluminum, and copper, are considered for a heat transfer analysis of the system. The results include temperature profiling, a comparison of the PVT system’s different components, and an overall output and efficiency study for all of the mentioned pipes. Results show that the obtained electrical and thermal efficiency for stainless steel is 0.1653 and 0.237, respectively, for aluminum it is 0.16515 and 0.2401, respectively, and for copper it is 0.16564 and 0.24679, respectively. After comparison, it was found that the overall efficiency for stainless steel is 0.40234, for aluminum is 0.40526, and for copper is 0.41244. Thus, this study will enhance the opportunity to provide an effective hybrid PVT energy management system.