# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 1 Introduction
### Review of Literature on Forecasting Techniques and Research Highlights  
The approach employed in this paper only uses other covariates in a wind meteorology dataset to forecast wind speed and does not extend to wind power forecasting using the power curve and other methods, as discussed in [8; 11]. In light of this, the authors present an account of forecasting techniques, the dataset used and conclusions based on the evaluation metrics used from relevant literature. The work of [PERSON] and [PERSON] [2] has the same source of wind meteorology dataset used for this research, using the ARIMA model, ANN and the adaptive neuro-fuzzy inference systems (ANFIS). The results show that ANN and ANFIS performed better for the ultra-short-term, while the ARIMA model performed better for the short-term, 1 h ahead wind speed and wind power forecasting using RMSE and MAE. A comparison of four wind forecasting models was done by [PERSON] et al. [4] involving ARIMA, hybrid ARIMA with a NN, ARIMA hybridised with two NNs and a NN on a SONDA dataset in all time scale forecast horizons. Using four evaluation metrics, the hybridised ARIMA with two neural networks outperformed the rest of the models. In [5], a wavelet-based NN forecast model, applicable to all seasons of the year, is used to predict the short-term wind power. Accurate forecasts using the normalised MAE (NMAE) and normalised RMSE (NRMSE) were recorded from the use of less historical data and a less complex model. A statistical approach to the wind power grid forecasting is discussed in [13]. The authors used wind scale forecasting modelling technology involving correlation matrix of output power and forecast accuracy coefficients. RMSE and MAE were used to evaluate the accuracy of the forecasts. A short-term wind farm power output prediction model using fuzzy modelling derived from raw data of wind farm is presented in [14]. This model was validated using the RMSE of the train set and the test set. The fuzzy model outperformed the NN model and was also able to provide an interpretable structure which reveals rules for the qualitative description of the prediction system. An investigation into accurate wind speed prediction using mathematical models is reported in [6]. The mathematical models used were the Holt-Winters, ANN and hybrid time series models on SONDA and SEINFRA/CE data. Using MAE and RMSE for model evaluation, the hybrid model presented lesser errors amongst the other models. An investigation comprising three types of backpropagation NN variants, Levenberg-Marquardt, Scaled conjugate gradient (SCG) and Bayesian regularisation, for a feed-forward multilayer perceptron was carried out by [PERSON] [15]. Using the statistical metrics of MAPE, SCG was found to outperform the rest. The Levenberg-Marquardt algorithm was used to train ANN ensembled with NWP to form a hybrid approach [7]. This outperformed the benchmark quantile regression (QR) based probabilistic method for wind power forecasting evaluated using NNAE and NRMSE [7]. The work of [PERSON] [16] on the use of Bayesian regularisation backpropagation algorithm to short-term wind power forecasting was seen as a viable technique for reducing model over-fitting. Quantifying uncertainty in forecasts due to variability drives the work of [PERSON] et al. [17]. Quantile regression averaging (QRA) method for generating prediction intervals (PIs) from combined point load forecasts generated from regression models on a publicly available dataset from Global Energy Forecasting Competition 2014 (GEFC0m2014) was used. Pinball loss function and Wrinkler score were used to evaluate the performance of this model for a day ahead forecast and recorded better PIs than did the benchmark vanilla methods [17]. [PERSON] et al. [18] combined probabilistic load forecasts using a constrained quantile regression averaging (CQRA) method, an ensemble, formulated as a linear programming (LP) problem. In this study, an ISO NE and CER dataset were used. The CQRA method outperformed the individual models [18].