Solar energy is a promising source of renewable energy, but its low efficiency, instability, and high manufacturing costs remain a big challenge. Recently, machine learning (ML) techniques have gained attention in the photovoltaic (PV) sector because of advances in computer power, tools, and data creation. The existing ML techniques used for fabrication and the different operational procedures of the PV sector have shown very impressive results with a higher degree of accuracy and precision. While previous studies have discussed ML techniques for PV fabrication or operational procedures, there is a lack of end-to-end research that covers the entire process from fabrication to implementation. In this paper, we present a comprehensive review of the application of ML in the field of solar energy, focusing on the development of new materials, enhancement of solar cell efficiency, implementation, and integration with the system, including fault detection, sizing, control, forecasting, management, and site adaptation. We evaluated more than 100 research articles, a significant proportion of which were published in the past three years. In our study investigating ML implementation in solar cell fabrication, we discovered that the Random Forest (RF), Linear Regression (LR), XGBoost, and Artificial Neural Network (ANN) algorithms are the most commonly employed techniques. Our findings demonstrate that XGBoost exhibits superior performance in optoelectronic prediction, while RF, LR, and ANN algorithms are better suited for predicting electrical parameters. Moreover, our analysis indicates recent ML research in this field explicitly emphasizes perovskite solar cells (PSCs). This work also discusses the challenges, directions, insights, and potential applications of ML for future PV system analysis.

The modern smart grid (SG) is mainly a cyber-physical system (CPS), combining the traditional power system infrastructure with information technologies. SG is frequently threatened by cyber attacks such as False Data Injection (FDI), which manipulates the states of power systems by adding malicious data. To maintain a reliable and secure operation of the smart grid, it is crucial to detect FDI attacks in the system along with their exact location. The conventional Bad Data Detection (BDD) algorithm cannot detect such stealthy attacks. So, motivated by the most recent deep learning (DL) developments and data-driven solutions, a new transformer-based model named XTM is proposed to detect and identify the exact locations of data intrusions in real-time scenarios. XTM, which combines the transformer and long short-term memory (LSTM), is the first hybrid DL model that explores the performance of transformers in this particular research field. First, a new threshold selection scheme is introduced to detect the presence of FDI, replacing the need for conventional BDD. Then, the exact intrusion point of the attack is located using a multilabel classification approach. A formally verified constraints satisfaction-based attack vector model was used to manipulate the data set. In this work, considering the temporal nature of power system, both hourly and minutely sensor data are used to train and evaluate the proposed model in the IEEE-14 bus system, achieving a detection accuracy of almost 100%. The row accuracy (RACC) metric was also evaluated for the location detection module, with values of 92.99% and 99.99% for the hourly and minutely datasets, respectively. Moreover, the proposed technique was compared with other deep learning models as well, showing that the proposed model outperforms the state-of-the-art methods mentioned in the literature.

Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.

In this paper the absorber layer of Tin mono-sulfide(SnS) thin film had been explored as an absorber layer of solar cell using the one dimensional simulator AMPS-1D.(Analysis of Microelectronic and Photonic Structures). The studies conducted by emphasize given to the SnS absorber layer thickness effect of the operating temperature and sun intensity with cell structure “ITO/ZnO/CdS/SnS/Ag”. The thickness of SnS thin film has been changed from 1000nm to 2000nm. The best efficiency of 13.2% is obtained at the highest thickness 2000nm at operating temperature is 3500C under 1sun. The study highlights the effect of thickness on the SnS absorber layer thin film solar cell.