Wind power prediction with accurate identification of ramp events based on Interval-SMOTE oversampling and ensemble learning

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Wind power ramp events (WPREs) are small probability events with serious wind power fluctuations, and it is one of the important factors leading to security accidents in the power grid. Firstly, given the small-sample nature of WPREs, this paper introduces an Interval-SMOTE oversampling method to increase the data points for ramp events; the generated points are confined within a dynamically adjusted interval that evolves with each iteration, thereby ensuring the maximum preservation of the original data trends. Then, in order to improve the detection efficiency of WPREs, an integration of Swinging Door Trending (SDT) algorithm is proposed to accurately identify the existing ramp events and non-ramp events in the original wind power sequence. Moreover, considering different types of WPREs, two different modeling methods of Stochastic Configuration Networks (SCNs) and Bidirectional Long Short-term Memory (BiLSTM) are employed to handle this problem. Due to the stochastic configuration and supervised mechanism of key parameters, SCNs can provide significant advantages in handling large samples, so it is applied to build the model of non-ramp events; as the unique structures of bidirectional processing of information, the BiLSTM has better ability in mining small sample information, so BiLSTM is applied to build the model of ramp events. The prediction results from the two models are then weighted to obtain the final results. Experimental results demonstrate that the proposed sampling method enhances accuracy metrics by 0.43% and 3.72% in different wind farms; specifically, regarding prediction accuracy as measured by RMSE, the proposed SCNs-BiLSTM model outperforms comparative models by 3.88% and 15.49% across various wind farms.