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Modeling and forecasting of Total Electron Content (TEC) values by an Artificial Neural Network model (ANNm) have high agreement on November 2003, 2004 superstorms. The work discusses Solar Wind Parameters (SWp) from OMNI (Operating Missions as a Node on the Internet) and TEC (TECU) data (International Reference Ionosphere) IRI-2012, IRI-2016 on November 20, 2003 (Dst = –422 nT) and on November 08, 2004 (Dst = –374 nT) Geomagnetic Storms (GSs). The paper commences with a 120-hour GS exhibition of SWp and proceeds with the correlation data of the variables, their hierarchical tracks, and inner dispersions. The ANNm with SWp as the input and TEC data as the output are introduced. The performance of the ANNm for 2003 and 2004 superstorms is adequate. The Correlation Coefficient (R) and Root Mean Square Error (RMSE) of the ANNm are 97.5%, 1.17 TECU (IRI-2012), and 97.9%, 1.09 TECU (IRI-2016) for the 2003 GS and 97.0%, 0.89 TECU (IRI-2012), and 98.0%, 1.61 TECU (IRI-2016) for 2004 GS. Parameters effect of the R constant of TEC data points out to the dynamic pressure (nPa), the magnetic field Bz component (nT), the flow speed (km/s), and the proton density (1/cm3). Besides, the absolute total error and the variance of the predicted TEC data for November 2003 and November 2004 GSs are 0.06 (0.30%) with 0.013 variance (IRI-2012), 0.09 (0.49%) with 0.016 variance (IRI-2016) for 2003 storm and 0.13 (0.73%) with 0.033 variance (IRI-2012), and 0.11 (1.06%) with 0.035 variance (IRI-2016) for 2004. It means that the paper models TEC data with considerable consistency over the SWp. [ABSTRACT FROM AUTHOR]

Option pricing based on data-driven methods is a challenging task that has attracted much attention recently. There are mainly two types of methods that have been widely used, respectively, the neural network method and the ensemble learning method. The option pricing model based on the neural network has high complexity, and a large number of hyper-parameters will be generated during training, resulting in difficult model adjustment. Furthermore, a lot of training data are needed. The option pricing model based on ensemble learning is not ideal for data feature extraction, because each calculation of the ensemble learning method is mainly to reduce the final residual. Therefore, this paper adopts a learning framework that embeds the modular ensemble learning methods into the network learning structure, and an option pricing model based on deep ensemble learning is proposed. The model is mainly composed of two parts: features reorganization based on random forest, used to calculate the importance of features, combined with the original data as training input; the multilayer ensemble data training structure is based on network learning structure and embeds two ensemble learning methods as network modules, and it also designs a stop algorithm to automatically determine the number of layers. This enables the model to retain the effect of data feature extraction and adapt to small and medium data sets without generating many hyper-parameters. Moreover, in order to make the model fully absorb the advantages of the two ensemble learning methods, we adopt cross-training for data. From the experimental results, it can be concluded that compared with the current optimal method, the prediction performance of the proposed model is improved by 36% in the root mean square error (RMSE), which proves the superiority of the proposed model from the quantitative direction. [ABSTRACT FROM AUTHOR]

As the development of smart grids is increasing, accurate electric load demand forecasting is becoming more important for power systems, because it plays a vital role to improve the performance of power companies in terms of less operating cost and reliable operation. Short-term load forecasting (STLF), which focuses on the prediction of few hours to one week ahead predictions and is also beneficial for unit commitment and cost-effective operation of smart power grids, is receiving increasing attention these days. Development and selection of an accurate forecast model from different artificial intelligence (AI)-based techniques and meta-heuristic algorithms for better accuracy is a challenging task. Deep Neural Network (DNN) is a group of intelligent computational algorithms which have a viable approach for modeling across multiple hidden layers and complex nonlinear relationships between variables. In this paper, a model for STLF using deep learning neural network (DNN) with feature selection is proposed. A wide range of intelligent forecast models was designed and tested based on multiple activation functions, such as hyperbolic tangent (tanh), different variants of rectifier linear unit (ReLU), and sigmoid. Among the others, DNN with leaky ReLu produced the best forecast accuracy. Regarding the precision of the methods used in this research work, certain output measures, such as absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) are used. There was also a reliance on multiple parametric and variable details to determine the capability of the smart load forecasting techniques. [ABSTRACT FROM AUTHOR]

It is important to accurately estimate the SOC to ensure that the lithium-ion battery is within a safe working range, prevent over-charging and over-discharging, and ultimately improve battery life. However, SOC is an internal state of the battery and cannot be directly measured. This paper proposes a SOC estimation method based on the wide and deep neural network model, which combines the linear regression (LR) model and the backpropagation neural network (BPNN) model. This article uses the dataset provided by the Advanced Energy Storage and Applications (AESA) group to verify the performance of the model. The performance of the proposed model is compared with the common BPNN model in terms of root mean square error (RMSE), average absolute proportional error (MAPE), and SOC estimation error. The validation results prove that the effect of the proposed model in estimating SOC is better than that of the ordinary BPNN model. Compared with the BPNN model, the RMSE values of the SOC predicted value of the wide and deep model in the charging and discharging stages were reduced by 10.2% and 15.4%, respectively. Experimental results show that the maximum SOC estimation error of the model in predicting the SOC during charging and discharging is 0.42% and 0.86%, respectively. [ABSTRACT FROM AUTHOR]

In a turning operation, involving removal of material from the outer diameter of a rotating cylindrical workpiece using a single-point cutting tool, there exist complex relationships between various cutting parameters and responses. In this paper, a turning operation under dry environment is considered with cutting speed, feed rate, and depth of cut as the input parameters, as well as material removal rate, average surface roughness, and cutting force as the responses. Dry turning operation reduces energy consumption and machining cost, thus eventually resulting in sustainable machining. For the considered process, the corresponding response values are envisaged using four prediction models, that is, multivariate regression analysis, fuzzy logic, artificial neural network, and adaptive neurofuzzy inference system (ANFIS), and their prediction performance is contrasted using five statistical metrics, that is, root mean squared percent error, mean absolute percentage error, root mean squared log error, correlation coefficient, and root relative squared error. It is noticed that ANFIS model consisting of the advantages features of both fuzzy logic and neural network outperforms the other prediction models with respect to the computed values of the considered statistical measures. Based on their acceptable values, it can be propounded that the ANFIS model can be effectively employed for prediction of process responses while treating different machining parameters as the input variables. [ABSTRACT FROM AUTHOR]