Machine learning-based wind speed time series analysis

Machine Learning-based forecasting analysis provides high-accuracy results in estimating renewable energy sources. Having an accurate forecast of wind energy is essential to manage storage resources due to seasonal and geographical differences. In addition, the challenges posed by the discontinuity and uncertainty of wind power require accurate forecasts for energy economists and data scientists. In this study, hourly average wind speed data covering the years 2019, 2020, and 2021 in California were used to perform a time series analysis and forecasting utilizing one of the AutoML tools, Fedot. In addition, RMSE, MAE, and MAPE results were evaluated in the analyzes performed. Estimation results are consistent with these statistical evaluations.