Broad Random Forest: A Lightweight Prediction Model for Short-Term Wind Power by Fusing Broad Learning and Random Forest

As an important component of sustainable development and energy transition, wind power is rapidly rising. This paper selects the time series of historical wind power as features and establishes a lightweight prediction model called a broad random forest model (BRF). The proposed model fully uses the feature representation ability of the broad learning system (BLS) and the fast computational speed of random forest (RF). To begin, the example sets are created with a sliding window for the wind power series. Then, the processed data are input into the BLS module. The feature-expansion function of BLS is fully utilized to generate mapped features and enhanced features. These two types of features are reconstructed to obtain a new sample set. Next, the RF model is established for the new sample set to make predictions. The prediction results of all decision trees are superimposed, and their average value is taken as the final prediction result. Finally, the predicted results of BRF are compared with other mainstream machine learning and deep learning methods. The experimental results show that the proposed model has the best predictive performance on the wind power datasets, with an improvement of 0.22% in R2 at least.