Wind speed forecast using random forest learning method
This work addresses wind energy forecasting for operational planning, but it is incremental as it applies an existing method to a specific dataset without major innovations.
The paper tackled wind speed forecasting for wind farm operations using a random forest regression model trained on two weeks of data with 12-hour historical inputs, achieving reliable short-term predictions up to three years ahead as indicated by root mean square error metrics.
Wind speed forecasting models and their application to wind farm operations are attaining remarkable attention in the literature because of its benefits as a clean energy source. In this paper, we suggested the time series machine learning approach called random forest regression for predicting wind speed variations. The computed values of mutual information and auto-correlation shows that wind speed values depend on the past data up to 12 hours. The random forest model was trained using ensemble from two weeks data with previous 12 hours values as input for every value. The computed root mean square error shows that model trained with two weeks data can be employed to make reliable short-term predictions up to three years ahead.