Medium-term wind power forecasting using reduced principal component analysis based random forest model

Due to its dependence on weather conditions, wind power (WP) forecasting has become a challenge for grid operators. Indeed, the dispatcher needs to predict the WP generation to apply the appropriate energy management strategies. To achieve an accurate WP forecasting, it is important to choose the appropriate input data (weather data). To this end, a medium-term wind power forecasting using reduced principal component analysis (RKPCA) based Random Forest Model is proposed in this paper. Two-stage WP forecasting model is developed. In the first stage, a Kernel Principal Component Analysis (KPCA) and reduced KPCA (RKPCA)-based data pre-processing techniques are applied to select and extract the important input data features (wind speed, wind direction, temperature, pressure, and relative humidity). The main idea behind the RKPCA technique is to use Euclidean distance for reducing the number of observations in the training data set to overcome the problem of computation time and storage costs of the conventional KPCA in the feature extraction phase. In the second stage, a Random Forest (RF) algorithm is proposed to predict the WP for medium-term. To evaluate the performance of the proposed RKPCA-RF technique it has been applied to data extracted from NOAA'S Surface Radiation (SURFRAD) network at Bondville station, located in USA. The presented results show that the proposed RKPCA-RF technique achieved more accurate results than the state-of-the-art methodologies in terms of RMSE (0.09), MAE (0.23), and R2 (0.85). In addition, the proposed technique achieved the lowest overall computation time (CPU).