Efficient modeling and forecasting of electricity prices are essential in today’s competitive electricity markets. However, price forecasting is not easy due to the specific features of the electricity price series. This study examines the performance of an ensemble-based technique for forecasting short-term electricity spot prices in the Italian electricity market (IPEX). To this end, the price time series is divided into deterministic and stochastic components. The deterministic component that includes long-term trends, annual and weekly seasonality, and bank holidays, is estimated using semi-parametric techniques. On the other hand, the stochastic component considers the short-term dynamics of the price series and is estimated by time series and various machine learning algorithms. Based on three standard accuracy measures, the results indicate that the ensemble-based model outperforms the others, while the random forest and ARMA are highly competitive.

This work inspects the thermal transportation of the magnetohydrodynamic Casson trihybrid nanofluid flow over a convectively heated bidirectional elongating sheet. The flow is considered as three dimensional passing over the sheet, which is placed in a porous medium. The effects of thermal radiations and space- and thermal-dependent heat sources are used in energy equations, while magnetic effects are used in momentum equations. Appropriate variables have been used to convert the modeled equations into a dimensionless form, which have then been solved using the homotopy analysis method. In this work, it is uncovered that both the primary and secondary velocities are weakened with an upsurge in porosity and magnetic factors. The thermal transportation is augmented with growth in thermal- and space-dependent heat source factors and the thermal Biot number. The convergence of the method used in this work is ensured through ℏ-curves. The results of this investigation have also been compared with the existing literature with a fine agreement among all the results that ensured the validation of the model and method used in this work.

The present study addressed the physical significance of the entropy generation for the mixed convection time-dependent flow of cross-hybrid nanoliquid due to the stretched surface at a stagnation point. The Plot for heat transport is discoursed by applying the role of thermal radiation under convective conditions. For hybrid nanofluid, engine oil is used as a base liquid with copper (II) oxide $CuO$ and titanium dioxide $Ti{O}_2$ nanoparticles. The existing model is framed in the highly partial differential equation system. The governing equations have been transformed into a set of ODS's using a similar scaling operation. Following this, the resulting ODEs are solved numerically through the BVP4c. The primary goal of this research is to analyze the results of varying the stretching ratio parameter ($\mathit{\lambda }$), Weissenberg parameter ($We$), thermal radiation ($Rd$), and Biot number ($Bi$) for both pure ${TiO}_2$ and CuO + ${TiO}_2$/ $EO$ hybrid nanofluid, on the velocity, temperature, drag force, heat transfer as well as entropy generation, and Bejan number was studied. A drop in velocity is observed with increasing values of the $We$ and upsurge in velocity for rising value of unsteady parameter ($A$), while increasing values of both of these parameters are associated with rising temperatures. Copper and titanium oxide nanoparticles are used to increase Engine oil (EO) thermal enactment, making it a more useful base fluid. Further, some significant industrial and engineering applications are related to the present problem discourse.

The balanced joint progressive censoring scheme gives guidelines for researchers in reducing the expense and period of the experiment with modified efficiency. In this paper, the stress-strength reliability model are considered and investigated, in which two independent samples are generated from Burr Type XII distributions under balanced joint progressive censoring scheme. In the classical analysis, the maximum likelihood estimate of the reliability parameter is derived, then the relevant confidence interval is provided based on the Fisher matrix. Under the Bayesian framework, the Lindley’s technique and hybrid Gibbs within Metropolis-Hastings algorithm are applied to acquire the estimates of the parameters, and the corresponding highest posterior density credible interval of the reliability model is also constructed. The reliability inference is realized by numerical simulation, and the validity of the inference method is analyzed based on the splashing data of silicone oil droplets impact (SSODI) under two surfaces wettability conditions.

The present communication develops the tools for Bayesian prediction of the Gompertz distribution based on CSPALT. The Metropolis-Hastings algorithm is applied to evaluate the BPIs for a censored sample based on unified hybrid censoring scheme. In order to investigate the impact of methodologies adopted, two numerical examples are performed. The simulated results show that reducing the censoring percentages causes smaller BPIs. The flexibility of the UHCS in evaluating the BPIs can be helped to overcome many difficulties in engineering problems.

The reliability of software has a tremendous influence on the reliability of systems. Software dependability models are frequently utilized to statistically analyze the reliability of software. Numerous reliability models are based on the nonhomogeneous Poisson method (NHPP). In this respect, in the current study, a novel NHPP model established on the basis of the new power function distribution is suggested. The mathematical formulas for its reliability measurements were found and are visually illustrated. The parameters of the suggested model are assessed utilizing the weighted nonlinear least-squares, maximum-likelihood, and nonlinear least-squares estimation techniques. The model is subsequently verified using a variety of reliability datasets. Four separate criteria were used to assess and compare the estimating techniques. Additionally, the effectiveness of the novel model is assessed and evaluated with two foundation models both objectively and subjectively. The implementation results reveal that our novel model performed well in the failure data that we examined.