RESUMEN
Renewable integration in utility grid is crucial in the current energy scenario. Optimized utilization of renewable energy can minimize the energy consumption from the grid. This demands accurate forecasting of renewable contribution and planning. Most of the researches aim to find a suitable forecasting model in terms of accuracy and error metrics. However, the uncertainty and variability in these forecasts are also significant. This work combines point forecast with interval forecast to provide comprehensive information about the forecast uncertainty. In this work, solar irradiance forecasting is carried out using artificial intelligence (AI) techniques. Forecasting is done using seasonal auto-regressive moving average with exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM) techniques and performance is evaluated. SVR model exhibited the best performance with R 2 values of 0.97 and 0.96 for winter and summer respectively and 0.85 for monsoon and post-monsoon seasons. This is followed by forecast error distribution studies and uncertainty analysis. For this, SVR forecast error data is fitted using laplace distribution. Uncertainty study is carried out using confidence intervals and coverage rates. Excellent coverage rates are obtained for various confidence levels for all seasons, indicating the appropriate fitting of error distribution. For the narrow 85% confidence band, coverage rates of 89%, 95%, 90%, and 88% are obtained for winter, summer, monsoon and post-monsoon respectively. The work emphasizes the need for error-distribution studies, modeling of forecast errors and their application in providing reliable forecast intervals with the perspective of enhancing system reliability.
RESUMEN
The process of image segmentation is partitioning an image into its constituent parts and is a significant approach for extracting interesting features from images. Over a couple of decades, many efficient image segmentation approaches have been formulated for various applications. Still, it is a challenging and complex issue, especially for color image segmentation. To moderate this difficulty, a novel multilevel thresholding approach is proposed in this paper based on the electromagnetism optimization (EMO) technique with an energy curve, named multilevel thresholding based on EMO and energy curve (MTEMOE). To compute the optimized threshold values, Otsu's variance and Kapur's entropy are deployed as fitness functions; both values should be maximized to locate optimal threshold values. In both Kapur's and Otsu's methods, the pixels of an image are classified into different classes based on the threshold level selected on the histogram. Optimal threshold levels give higher efficiency of segmentation; the EMO technique is used to find optimal thresholds in this research. The methods based on an image's histograms do not possess the spatial contextual information for finding the optimal threshold levels. To abolish this deficiency an energy curve is used instead of the histogram and this curve can establish the spatial relationship of pixels with their neighbor pixels. To study the experimental results of the proposed scheme, several color benchmark images are considered at various threshold levels and compared with other meta-heuristic algorithms: multi-verse optimization, whale optimization algorithm, and so on. The investigational results are illustrated in terms of mean square error, peak signal-to-noise ratio, the mean value of fitness reach, feature similarity, structural similarity, variation of information, and probability rand index. The results reveal that the proposed MTEMOE approach overtops other state-of-the-art algorithms to solve engineering problems in various fields.
RESUMEN
Any industry that manufactures dies, punches, molds, and machine components from difficult-to-cut materials, such as Inconel, titanium, and other super alloys, largely relies on wire electrical discharge machining (WEDM). In the current study, the effect of the WEDM process parameters on Inconel 600 alloy with untreated zinc and cryogenically treated zinc electrodes was investigated. The controllable parameters included the current (IP), pulse-on time (Ton), and pulse-off time (Toff), whereas the wire diameter, workpiece diameter, dielectric fluid flow rate, wire feed rate, and cable tension were held constant throughout the experiments. The significance of these parameters on the material removal rate (MRR) and surface roughness (Ra) was established using the analysis of the variance. The experimental data acquired using the Taguchi analysis were used to analyze the level of influence of each process parameter on a particular performance characteristic. Their interactions with the pulse-off time were identified as the most influential process parameter on the MRR and Ra in both cases. Furthermore, a microstructural analysis was also performed via scanning electron microscopy (SEM) to examine the recast layer thickness, micropores, cracks, depth of metal, pitching of metal, and electrode droplets over the workpiece surface. In addition, energy-dispersive X-ray spectroscopy (EDS) was also carried out for the quantitative and semi-quantitative analyses of the work surface and electrodes after machining.