This paper aims to present an economic decision-making model for determining the optimal wind turbine (WT) design for different bus nodes in a Radial Distribution Network (RDN) based on the wind potential of the studied site and grid capability. The main objective function in the optimization problem of this study is the maximization of the Net Present Value (NPV) of wind energy incomes subject to the WT geometrical design variables, including the rotor diameter and Tower Height; and under the RDN constraints to maintain the power system stability. Adequate placements among the different bus nodes for WT installation are those with the maximum NPV value. Furthermore, the intermittent characteristic of wind energy leads to the use of an Artificial Neural Network (ANN) in wind speed forecasting for good estimation of the generated wind energy. The effectiveness of the proposed model was validated using IEEE 9 and IEEE 33 Bus RDNs. The results demonstrate that the WT design determination is not related to the power of the wind potential but mostly to the capability of the connected RDN.

To face the growing electricity demand, several countries have adopted the solution of clean energy and use renewable energy sources (e.g., wind and solar) to reinforce the stability of the power network, especially during peak demand periods. Forecasting wind power generation is one of the important tasks for the network regulator. This paper deals with the probabilistic forecasting of hourly wind speed time series. In this approach, instead of evaluating a single-point forecast, an intraday interval prediction is provided, which allows modeling the probability distribution of the wind speed process at any specific hour. Practically, the quantification of uncertainty might be of particular interest for risk management purposes associated with wind power generation. The definition of interval prediction is based on the notion of conditional quantiles. In this paper, we introduce a new statistical approach, which deals with the nonstationarity behavior of the wind speed process, to define the conditional quantile predictor. The proposed approach was applied and evaluated on hourly wind speed processes. The suggested methodology provides accurate single-point forecasts using the conditional median as the predictor. Furthermore, the obtained hourly interval predictions are small and well adapted to the shape of the daily wind speed curves, which confirms the efficiency of the proposed approach.

This paper introduces novel nonparametric supervised learning techniques for classifying massive datasets, addressing key limitations of existing methods in Big and Streaming Data framework. We propose an offline kernel-based classifier enhanced by Batch Principal Component Analysis (PCA) for dimensionality reduction to mitigate the “curse of dimensionality”. Additionally, an online classifier is developed for streaming data, combining online PCA with a kernel-based recursive classifier using a stochastic approximation algorithm. Application to fetal well-being monitoring demonstrates that the online classifier achieves a competitive median misclassification rate (11.92%), comparable to the offline classifier (11.54%) and Random Forest (11.31%), while requiring only 1/15th of the offline classifier’s computation time. Receiver Operating Characteristic (ROC) analysis shows superior Area Under the Curve (AUC) for the offline classifier but at a significant computational cost. A second study on larger database of credit scoring confirms these findings, showing that the online classifier achieves an F1-score of 96.40% and an accuracy of 93.08%, closely matching the performance of neural networks (96.46%, 93.22%) and boosting (96.51%, 93.31%). Notably, the online classifier accomplishes this with a CPU time of only 0.87 seconds per classification - over 600 times faster than neural networks - demonstrating its effectiveness for high-frequency, real-time financial decision-making.

This paper explores the nonparametric estimation of the volatility component in a heteroscedastic scalar‐on‐function regression model, where the underlying discrete‐time process is ergodic and subject to a missing‐at‐random mechanism. We first propose a simplified estimator for the regression and volatility operators, constructed solely from the observed data. The asymptotic properties of these estimators, including the almost sure uniform consistency rate and asymptotic distribution, are rigorously analyzed. Subsequently, the simplified estimators are employed to impute the missing data in the original process, enhancing the estimation of the regression and volatility components. The asymptotic behavior of these imputed estimators is also thoroughly investigated. A numerical comparison of the simplified and imputed estimators is presented using simulated data. Finally, the methodology is applied to real‐world data to model the volatility of daily natural gas returns, utilizing intraday EUR/USD exchange rate return curves sampled at a 1‐h frequency.