This article proposes an informative exploratory tool, the functional boxplot, for visualizing functional data, as well as its generalization, the enhanced functional boxplot. Based on the center o...

Accurately detecting Parkinson’s disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying therapy. Towards this end, the premotor stage in PD should be carefully monitored. An innovative deep-learning technique is introduced to early uncover whether an individual is affected with PD or not based on premotor features. Specifically, to uncover PD at an early stage, several indicators have been considered in this study, including Rapid Eye Movement and olfactory loss, Cerebrospinal fluid data, and dopaminergic imaging markers. A comparison between the proposed deep learning model and twelve machine learning and ensemble learning methods based on relatively small data including 183 healthy individuals and 401 early PD patients shows the superior detection performance of the designed model, which achieves the highest accuracy, 96.45% on average. Besides detecting the PD, we also provide the feature importance on the PD detection process based on the Boosting method.

Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array’s maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.Peer ReviewedPostprint (author's final draft

This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array's residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading

Wind power represents a promising source of renewable energies. Precise forecasting of wind power generation is crucial to mitigate the challenges of balancing supply and demand in the smart grid. Nevertheless, the major difficulty in wind power is its high fluctuation and intermittent nature, making it challenging to forecast. This study aims to develop efficient data-driven models to accurately forecast wind power generation. Crucially, the main contributions of this work are listed in the following major elements. Firstly, we investigate the performance of enhanced machine learning models to forecast univariate wind power time-series data. Specifically, we employed Bayesian optimization (BO) to optimally tune hyperparameters of the Gaussian process regression (GPR), Support Vector Regression (SVR) with different kernels, and ensemble learning (ES) models (i.e., Boosted trees and Bagged trees) and investigated their forecasting performance. Secondly, dynamic information has been incorporated in their construction to further enhance the forecasting performance of the investigated models. Specifically, we introduce lagged measurements to enable capturing time evolution into the design of the considered models. Furthermore, more input variables (e.g., wind speed and wind direction) are used to further improve wind prediction performance. Actual measurements from three wind turbines in France, Turkey, and Kaggle are used to verify the efficiency of the considered models. The results reveal the benefit of considering lagged data and input variables to better forecast wind power. The results also showed that the optimized GPR and ensemble models outperformed the other machine learning models.