###### Abstract
Different neural network (NN)-based frameworks [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34] were proposed for the wind generation forecast, e.g., artificial neural networks (ANN) [17], a wavelet neural network (WNN) [19], an adaptive neuro-fuzzy neural network (ANFIS) [18], the long short-term memory (LSTM) model [25], a convolutional neural network (CNN) [21; 22; 26], radial neural networks [23], a fuzzy wavelet neural network [24], a deep echo state network [20], a genetic LSTM [27], a K-shape- and K-means-guided deep convolutional recurrent network [28], a dynamic elastic NET (DELNET) [29], an attention temporal convolutional network (ATCN) [30], a spatio-temporal correlation model (STCM) based on convolutional neural networks long short-term memory (CNN-LSTM) [32], the extended deep sequence-to-sequence long short-term memory regression (STSR-LSTM) [33], a hybrid model with attention mechanism and complete ensemble empirical mode decomposition (CEEMDAN) [34], etc. Although neural network (NN)-based methods may enhance the forecast accuracy to a certain degree, the existing NN-based approaches may have poor performance during ramp events, simply because the ramp and non-ramp events are not separated when training the NNs. It has been shown that NNs may perform poorly if extreme (or ramp) events are overlooked [35]. Previous studies [36; 37] have revealed (1) the non-stationary and seasonal dynamics of wind farm generation and (2) the heterogeneous dynamics of non-ramp and ramp events. Moreover, as different classes of wind turbines are deployed in wind farms, we observe that the dynamics of the wind generation of different classes of wind turbines can be different (see Section 2). Thus, employing NNs without considering these distinct features of wind farm generation means wind farm generation cannot be accurately forecast, especially for ramp events (see Section 4). In the previous work, seasonal self-evolving neural networks [38] are built for different seasons and ramps are defined using fixed thresholds. However, it is observed that the dynamics of wind ramps may change within each season, and due to the time-varying dynamics of wind ramps, it is challenging to use fixed thresholds to accurately capture the dynamics of the wind ramps. To address this challenge, this paper proposes a dynamic threshold-based approach that can adapt to the time-varying dynamics of wind ramps. Specifically, we propose Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks that account for the time-varying dynamics of different wind turbines' power outputs during non-ramp and ramp events in order to achieve a better wind farm generation prediction. First, the proposed DSPOT approach leverages dynamic ramp thresholds to classify the wind generation data of each class of wind turbines into ramp and non-ramp datasets, which can account for the time-varying dynamics of the ramp and non-ramp events across different classes of wind turbines. Then, different NNs are trained for each dataset to learn the heterogeneous dynamics of the different classes of wind turbines' generation, in which the NeuroEvolution of Augmenting Topologies [39] is adopted to evolve the NNs in order to obtain the best network topology and weighting parameters. As the efficacy of NNs depends on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-basedapproach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the NNs. _Ultimately, the proposed DSPOT-enhanced self-evolving neural networks (see Figure 1) form a closed loop for optimizing the performance of the wind generation forecast purely based on the data._  
Real-world wind farm generation measurements often exhibit distinct features, such as the non-stationarity and the heterogeneous dynamics for ramp and non-ramp events across different classes of wind turbines.