# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 1 Introduction  
### Background  
The reduction in conventional energy sources, skyrocketing prices of fossil fuels, along with the attendant effect on environmental degradation and pollution from the emission of greenhouse gases (GHG), as well as global warming, necessitates the use of renewable energy sources [1; 2]. Amongst various forms of renewable energy sources, the wind is an efficient, affordable, pollution-free, renewable and abundant energy source [3; 4]. Recently, wind energy generated in the world has increased to a 250-GW cumulative wind power capacity as of 2012, and it is projected to increase to 800 GW by 2021 according to world wind energy association (WWEA) [3; 4]. Wind energy generationwith many promising prospects, however, is faced with the challenge of the variability of the wind speed. The fluctuating, intermittent and stochastic nature of the wind makes predicting power generation a huge task [5]. In addition, the non-linear, non-stationary characteristics of the wind speed temporal series make accurate forecasting of power generation difficult [4]. Wind speed is the main wind information amongst others. Its predictability is essential for assessing wind energy exploitation purpose such as wind power generation. Hence, accurate wind speed prediction helps in maximising wind power generating facilities by reducing mistakes and economic cost involved in the planning and effective running of such facilities [6]. Wind speed prediction methods for electrical energy exploitation purpose gained attention in recent research. Most literature lists different methods for wind speed/power forecasting some of which are the persistence, numerical, statistical and hybrid methods [7; 8]. The statistical methods are seen as including the artificial intelligence (AI) methods, especially the artificial neural networks (ANN) method. ANN is a black-box statistical method and non-ANN methods are seen as grey-box statistical methods [8]. Other authors, however, classify the AI methods as being non-statistical and broadly classify wind speed/power forecasting methods into conventional statistical methods and AI methods [2; 4; 9]. Statistical methods are based on statistical time series using the previous history of wind data to forecast over the next short period, say, 1h. Models based on statistical methods are easy to use and develop. They adjust their parameters through the difference between their predicted and actual wind speed [4]. AI methods, on the other hand, make use of machine learning (ML) models such as neural networks (NN) and gradient boosting machines (GBMs) [2]. ML-based models make use of non-statistical approaches in knowing the relationship between input and output [4; 10]. Statistical methods and ML methods, especially the NN methods, are both suited for short-term forecasting. The persistence method is used for very short-term predictions in the operation of wind turbines. The physical models make use of mathematical models of the atmosphere (such as numerical weather prediction (NWP)) and statistical distributions on physical quantities such as barometric pressure for forecasting. The hybrid models use a combination of any of these models especially the statistical models and machine learning models for forecasting [4; 7]. Several forecasting techniques have been studied and further classified based on time scales and the type of model. While some authors used different time horizons in classifying forecast time scales into ultra short-term (few seconds or minutes), short-term (from minutes into hours), medium-term (hours and days) and long-term (weeks, month and year) [2; 4], others classify forecasts ranging from hours to few days as short-term forecasting time horizon [8; 9]. On the other hand, wind forecasting models are classified into deterministic and probabilistic forecasting models [11]. Moreover, an investigation into techniques for forecasting wind speed useful for wind power generation is herein reported.