## 1 Introduction  
Installation of renewable energy--predominantly wind energy--has received a great deal of attention globally due to several environmental protocols agreed upon by almost all countries as the primary directives of the United Nation (UN). This is due the fact that wind power generation is a zero-carbon generation method, is accessible everywhere, requires a smaller installation space, etc. Moreover, the advent of power electronics and the associated control technology has further accelerated the rapid deployment of wind generation systems globally. Wind energy is a promising source of energy for the future generation smart grid. This is mainly due to the cleanness and the wide availability of wind energy. The recent and rapid development of power electronic converter technology is the other main reason for the promising integration of wind energy in the future smart grid. Although wind power generation has significant environmental advantages and is a promising source of energy for the future, its uncertainty due to intermittency of weather variables makes it worth less than the conventional generation sources. This is because the generation power uncertainty causes large problems with power grid stability and control. However, this problem is not unmanageable. To harness the benefit and increase the competitiveness of wind energy, an enhanced forecast of wind generation is essential. An accurate wind power forecast enhances control, stability, sustainability, and flexibility of electricity grids with a large proportion of wind generation. Accurate forecasts allow the various stakeholders involved in the power industry to make better decisions regarding power system investment, planning, operation, management, economics, market, strategy, risk analysis, etc. Thus, accurate 24 h-ahead (called short-term) prediction of wind generation plays a key role in the power systems containing a huge penetration of wind energy. Plenty of approaches and enabling tools have been implemented in the last few years to forecast wind speed and the associated energy output. These methods are grouped as statistical, physical, and time-series methodologies according to the estimation approaches the techniques use [1]. A number of research groups nowadays use the integration of statistical and physical approaches to find improved hybrid approaches that can be used for a longer horizon of forecast. In the hybrid approaches, the statistical approaches play auxiliary roles on the forecast input dataset gathered by the physical approaches. Although these two approaches have become very common for wind power prediction (WPP) [2; 3], as mentioned above, the hybridization of statistical and physical techniques has become widely implementable and more effective [4; 5]. Additionally, several correlation-based methods have been devised for wind energy prediction for obtaining lower forecast accuracy [6]. Contrarily, via time and the emergence of higher-level mathematical programming tools [such as artificial intelligence (AI), machine learning (ML), and deep learning (DL)], further innovative approaches have been devised for WPP. Namely, neural networks [7; 8; 9], neural networks with Gaussian approximation and Bayesian training [10], neural networks with wavelet transform (WT) [11], fuzzy system-based techniques [5; 12], Kalman filter [13], support vectors [14], and neural networks combined with fuzzy logic system [15] were devised for WPP. From the aforementioned previous works on WPP, the ML or the AI based methods have given better forecast accuracy over the other approaches. However, most of these methods have only used the wind power generation historical measurement records and did not consider environmental factors (weather variable forecast values) while developing the forecast models. These methods are faced with major problems when the historical wind power measurement dataset has skipped or provides erroneous entries, and they are consequently incapable of giving accurate predictions.