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With the increasing integration of wind power systems into conventional power systems, an accurate wind speed (WS) forecasting technique is essential for the reliable and stable operation of the power grid. The availability of wind energy increases with the correct WS estimation technique. Linear statistical methods such as the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model have been popular in recent times for short-term and very short-term forecasting of WS. However, studies are continuing the effect of forecast errors resulting from different disaggregated time series on the resulting WS forecast error. In this paper, we present a state-of-the-art machine learning model, the Prophet model, which has not been used in short-term wind forecasting before in the literature. The performance reliability and accuracy of the developed Prophet model’s results were compared to those of the SARIMA model. The success of forecasting time-series data was evaluated using Mean Absolute Error (MAE) and Mean Square Error (RMSE) results. The models’ reliability, accuracy, fit, and performance were evaluated using standardized residuals, autocorrelation function (ACF), and partial autocorrelation (PACF). The 7-day performance metrics of the Prophet model are 1.9, 2.5, 4.1, 3.8, 4.0, 8.4, and 2.1 for MAE, and 2.3, 2.9, 4.7, 5.0, 4.4, 8.6, and 2.5 for RMSE, respectively. The performance measures of the SARIMA model are 3.3, 4.1, 5.1, 4.8, 5.1, 1.8, and 5.4 for the MAE, and 4.2, 5.1, 6.2, 6.2, 5.7, 2.6, and 6.2 for the RMSE, respectively. The Prophet model gave higher accuracy than SARIMA for all other days except 1 day (06.01.2018). As a result, the Prophet model outperformed the SARIMA model in predicting WS measurements 24 h ahead using quarterly training data.

A new approach to short-term wind speed prediction: the prophet model