# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 1 Introduction
### Review of Literature on Forecasting Techniques and Research Highlights
[PERSON] and [PERSON] [19] investigated interval forecasting using QRA to construct PIs on a dataset from GDF Suez website. The QRA method outperformed the 12 constituent models using PI coverage percentage (PICP) and PI width (PIW) in forecasting electricity spot prices. Using extreme learning machine, optimised with a two-step symmetric weighted objective function and particle swarm optimisation, a deterministic forecast with a quantifiable prediction uncertainty was used by [PERSON] et al. [20]. This was carried out on benchmark datasets and real-world by-product gas datasets. The results using PICP, prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD) showed high-quality PIs constructed for by-product gas forecasting application. Using wavelet neural networks (WNN), [PERSON] et al. [21] quantified the potential uncertainties of wind power forecasting via constructing prediction intervals using wind power data on an Alberta interconnected electric system. The prediction intervals constructed were evaluated using PICP and PI covered-normalised average width (PICAW). Indices such as coverage width-based criterion (CWC) and PI multi-objective criterion (PIMOC) including PINAW, PICAW and PICP were also used to evaluate their models. The PIs constructed from PIMOC were the most accurate compared to those based on the other prediction interval methods. The EKMOABC optimised WNN is the proposed method compared with WNN optimised with multi-objective particle swarm optimisation (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) [21]. The need to quantify the uncertainty and risk associated with point forecasts through probabilistic forecasting drives the work of [PERSON] and [PERSON] [22]. Using an ensemble learning tool, the random forest for combining individual models and hourly-ahead combined point forecasts were obtained. This was used for obtaining the ensemble based probabilistic solar power forecasts. The comparison carried out on one year of Australian year data showed that the ensemble-based and analogue ensemble-based probabilistic forecast have similar accuracy using the pinball loss function. For extensive reviews on probabilistic wind power forecasting, wind power generation and wind energy forecasting management and operational challenges, see [3; 8; 23], respectively. GAMs are suitable for exploring the dataset and visualising the relationship between the dependent and independent variables [24]. [PERSON] et al. [25] used GAMs in modelling electricity demand for the French distribution network at both short- and medium-term time scales for more than 2200 substations. Drivers of the load consumption were modelled using GAM and compared with the operational one in [26]. GAM is good for interpretability, regularisation, automation and flexibility [24]. It finds a balance between the biased and yet interpretable algorithm, linear models and extremely flexible black-box learning algorithm composing the movement, seasonality and climate change variables. GAM was fitted on weekly load demand in [27]. The existence of functional form trend between two variables and their shape whether linear or non-linear, should it exist, was examined using GAMs by [PERSON] et al. [28]. Given the approaches used in existing literature for various kinds of forecasting, it is evident that the methods employed in this study have been used on different datasets around the world and evaluated, mostly using the same metrics we employ. The approach involves a comparative use of three such methods for point forecasting and two other methods for combining forecasts in which one of these two methods were used for interval forecasting using a dataset created and curated in South Africa. This was done to ascertain the viability of our methods for point forecasting of wind speed and interval forecasting. Another highlight in this paper is the use of GAMs for wind speed forecasting. We observe a dearth of GAM for wind speed forecasting in literature.