## 1 Introduction  
Wind energy is a variable renewable energy source [1] and the power produced by the wind turbine hence fluctuates with the variation of wind speed [2]; therefore, in wind farms, unexpected variations of wind power output may increase the operating costs of the electricity system. So, intermittency of wind is the biggest challenge for a wind farm to implement wind energy as a reliable autonomous source of electric power [3]. Moreover, a wind speed forecasting (WSF) system based on an accurate model that reflects the variation of wind speed is critical to effective wind energy harvesting, integration of available wind power into the electrical power grid, and analyzing the efficiency and performance of wind-turbine-based electrical generation systems [4]. Despite the development of various WSF methods, accurately predicting wind speed still remains a challenge. Furthermore, the length of the forecast horizons correlates with the accuracy of forecasting techniques. Wind speed prediction can have various implications requiring different time scales. For example, turbine control often necessitates a response time of seconds or fractions of seconds, whereas grid integration production planning and market response require longer time horizons. The time scale of prediction also differs according to the energy markets. The real-time energy market requires a response in minutes, whereas the day-ahead energy market requires the prediction up to 24 h ahead as it requires information for energy trading for the next day [5; 6]. There may be a requirement for different time scale forecast horizons in between these two-time scales. For example, economic load dispatching and load increment/decrement decisions require a time scale of 30 min to 6 h ahead [3]. Wind speed prediction models have been classified mainly into four categories in the literature: (a) The persistence model, in which future wind speed is deemed to be equal to the wind speed at the forecasting time [7]. It is an economical and simple method that can be adopted by almost everyone to serve as the base model for comparing forecasted values by other methods and its main drawback is its unsuitability for forecasting more than a one-time step of forecast horizons; (b) The physical method, in which numerical weather prediction (NWP) is used by incorporating complex atmospheric characteristics, including temperature, pressure, and wind shear into wind speed predictions [8]. For long-term forecasts, NWP produces precise estimates that are generally applied over vast areas. However, since numerical weather prediction models are memory- and time-intensive, they are not ideal candidates for short time horizons forecasting; (c) Statistical methods, in which one can explore the mathematical relationship between the various features of the wind time series data. This method includes the following models: Weibull-probability-density-based forecasting (WEB), autoregressive integrated moving average (ARIMA), and the Bayesian probability density function (BBM) approach. These models are mostly used for short forecast horizons and are not suitable for longer forecast horizons due to their non-linearity assumption for wind data; and (d) Artificial intelligence, which includes neural networks (ANN) [9; 10], regression or decision trees (RT) [11; 12], support vector regression (SVR) [13; 14; 15; 16], and recurrent neural networks (RNN) [17; 18; 19]. This study was inspired by the work of [PERSON] et al., [PERSON] et al., and [PERSON] et al. [20; 21; 22], where the idea of a probability-density-function-based wind speed prediction model was introduced. In this study, the performance of various univariate models is compared, and an error correction algorithm is proposed for the probability-density-based wind speed prediction model.