## 1 Introduction  
Increasing demand for energy, global warming, and other adversarial effects on the environment due to fossil fuel sources result in a growing reliance on renewable energy resources. Renewable energy resources are crucially important to minimize this effect and reduce conventional fuel dependence. Especially, parties of the Paris Climate Agreement countries pledge to reduce greenhouse gas emissions. Wind energy is gaining growing attention due to eligibility and zero emission. In the last two decades, the wind energy potential has become well-known all around the world. The number of wind farms is exponentially increasing due to decreases in maintenance and operation costs and increases in the reliability of wind turbines [1]. Nevertheless, wind energy's intermittent and uncertain nature affects the dependability of wind farms [2,3]. Therefore, the predictability of wind energy is becoming increasingly crucial [4].  
In the past decade, research on wind speed forecasting has also been growing. Forecasting methods are generally classified as physical, statistical, machine learning, and hybrid forecasting methods [5]. Physical methods exploit geophysical fluid dynamics and thermodynamics principles to derive mathematical equations. Most physical methods rely on the NWP (numerical weather prediction system) model. NPW models require tremendous computational time due to their complex mathematical formulation [6]. Therefore, NPW models are usually used for long-term prediction and are difficult to apply in wind power forecasting for short-term.  
Statistical models use statistical assumptions to reveal a linear relevance between observed wind data and wind speed for forecasting. Most of the statistical models fail to capture the nonlinear attributes of the wind data [7]. Artificial intelligence (AI) models gain popularity in wind forecasting due to the ability to learn the input-output relation using past data. In particular, AI models reveal the complex nonlinear relationships from observed data and recognize hidden patterns to forecast future values [8]. Another advantage of AI models is the ability to adjust the model based on changing trends in the data [9]. Although each particular AI method has some advantages, each individual model has its own limitations [5]. Thus, hybrid or combined models employ the benefits of different methods.  
This study proposes a novel forecasting approach for wind speed with time-series features clustering-based deep learning. The proposed model utilizes the Dirichlet mixture model and dynamic time warping to cluster time-series data features and deep learning in forecasting. In particular, clustering is performed on the features which capture the temporal relationship in time series. Moreover, the proposed ensemble models integrate the results from the models applied to the clustered and entire data set (local model and global model, respectively). We expect that the global model captures the long-term patterns in the data while the local model captures the short-term patterns. To the best of our knowledge, no study in the wind speed forecasting domain has clustered the time series data by using the Dirichlet mixture model and dynamic time warping. This research contributes to closing this research gap.  
In Section 2 of this paper, we present the relevant literature review on wind speed forecasting and briefly discusses speed forecasting methods' limitations. Next, relevant methodologies are presented in Section 3. Then, in Section 4, we present the proposed wind speed forecasting methods. The experimental design and computational results are presented in Section 5. Finally, in Section 6, we shortly address the conclusions and future research directions.