In recent years, the supply of electric vehicles, which are eco-friendly cars that use electric energy rather than fossil fuels, which cause air pollution, is increasing. Accordingly, it is emerging as an urgent task to predict the charging demand for the smooth supply of electric energy required to charge electric vehicle batteries. In this paper, to predict the charging demand, time series analysis is performed based on two types of frames: One is using traditional time series techniques such as dynamic harmonic regression, seasonal and trend decomposition using Loess, and Bayesian structural time series. The other is the most widely used machine learning techniques, including random forest and extreme gradient boosting. However, the tree-based machine learning approaches have the disadvantage of not being able to capture the trend, so a hybrid strategy is proposed to overcome this problem. In addition, the seasonal variation is reflected as the feature by using the Fourier transform which is useful in the case of describing the seasonality patterns of time series data with multiple seasonality. The considered time series models are compared and evaluated through various accuracy measures. The experimental results show that the machine learning approach based on the hybrid strategy generally achieves significant improvements in predicting the charging demand. Moreover, when compared with the original machine learning method, the prediction based on the proposed hybrid strategy is more accurate than that based on the original machine learning method. Based on these results, it can find out that the proposed hybrid strategy is useful for smoothly planning future power supply and demand and efficiently managing electricity grids.

In many life-testing and reliability studies, the experimenter might not always obtain complete information on failure times for all experimental units. Multiply Type-II censored sampling arises in a life-testing experiment whenever the experimenter does not observe the failure times of some units placed on a life-test. In this paper, we obtain estimators for the entropy function of a double exponential distribution under multiply Type-II censored samples using the maximum likelihood estimation and the approximate maximum likelihood estimation procedures. We compare the proposed estimators in the sense of the mean squared errors by using Monte Carlo simulation.

This paper derives the entropy of a generalized half-logistic distribution based on Type-II censored samples, obtains some entropy estimators by using Bayes estimators of an unknown parameter in the generalized half-logistic distribution based on Type-II censored samples and compares these estimators in terms of the mean squared error and the bias through Monte Carlo simulations.

Estimation for the Half Logistic Distribution under Progressive Type-II Censoring Approximate maximum likelihood estimator;half logistic distribution;progressive Type-II censoring;reliability; In this paper, we derive the approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator of the scale parameter in a half-logistic distribution based on progressive Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples. We also obtain the approximate maximum likelihood estimators of the reliability function using the proposed estimators. We compare the proposed estimators in the sense of the mean squared error.

The maximum likelihood method is the most widely used estimation method. On the other hand, it can produce substantial bias, and an approximate confidence interval based on the maximum likelihood estimator cannot be valid when the sample size is small. Because the sizes of the record values are considerably smaller than the original sequence observed in the majority of cases, a method appropriate for this situation is required for precise inference. This paper provides the exact confidence intervals for unknown parameters and exact predictive intervals for the future upper record values by providing some pivotal quantities in the two-parameter Rayleigh distribution based on the upper record values. Finally, the validity of the proposed inference methods was examined from Monte Carlo simulations and real data.