Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because of their impressive characteristics, including their high power and energy density, extended lifespan, significant cycling stability, and quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance and lifespan of supercapacitors to choose the appropriate materials and develop plans for replacement. Carbon-based supercapacitor electrodes are crucial for the advancement of contemporary technology, serving as a key component among numerous types of electrode materials. Moreover, accurately forecasting the lifespan of energy storage devices may greatly improve the efficient handling of system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches for predicting the performance of energy storage materials. The interest in machine learning is driven by its noteworthy benefits, such as improved accuracy in predictions, time efficiency, and cost-effectiveness. This paper reviews different charge storage processes, categorizes SCs, and investigates frequently employed carbon electrode components. The performance of supercapacitors, which is crucial for Internet of Things (IoT) applications, is affected by a number of their characteristics, including their power density, charge storage capacity, and cycle longevity. Additionally, we provide an in-depth review of several recently developed ML-driven models used for predicting energy substance properties and optimizing supercapacitor effectiveness. The purpose of these proposed ML algorithms is to validate their anticipated accuracies, aid in the selection of models, and highlight future research topics in the field of scientific computing. Overall, this research highlights the possibility of using ML techniques to make significant advancements in the field of energy-storing device development.

Environmental sensors are utilized to collect real-time data that can be viewed and interpreted using a visual format supported by a server. Machine learning (ML) methods, on the other hand, are excellent in statistically evaluating complicated nonlinear systems to assist in modeling and prediction. Moreover, it is important to implement precise online monitoring of complex nonlinear wastewater treatment plants to increase stability. Thus, in this study, a novel modeling approach based on ML methods is suggested that can predict the effluent concentration of total nitrogen (TN_eff) a few hours ahead. The method consists of different ML algorithms in the training stage, and the best selected models are concatenated in the prediction stage. Recursive feature elimination is utilized to reduce overfitting and the curse of dimensionality by finding and eliminating irrelevant features and identifying the optimal subset of features. Performance indicators suggested that the multi-attention-based recurrent neural network and partial least squares had the highest accurate prediction performance, representing a 41% improvement over other ML methods. Then, the proposed method was assessed to predict the effluent concentration with multistep prediction horizons. It predicted 1-h ahead TN_eff with a 98.1% accuracy rate, whereas 3-h ahead effluent TN was predicted with a 96.3% accuracy rate.