###### Abstract  
This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events. ramp events; short-term wind power forecast; distributional forecast; point forecast; Bayesian optimization; self-evolving neural networks +
Footnote †: journal: _Future Internet_  
There are many studies on short-term wind power forecast using time-series models (e.g., the autoregressive model [4], autoregressive moving average model [5], Gaussian process (GP) [6], Kalman filtering (KF) [7], and Markov chains [8]). However, these studies cannot effectively capture the non-stationarity and the heterogeneous dynamics of wind farm generation. To address the problem of non-stationary wind generation, the empirical mode decomposition (EMD), complementary empirical mode decomposition (CEEMD) [9], improved complete ensemble empirical mode decomposition (iCEEMDAN) [10], hybrid model of LSTM and variational mode decomposition (VMD) [11; 12], and ensemble empirical mode decomposition (EEMD)-based hybrid methods [13] are proposed, which use Intrinsic Mode Functions (IMFs) as a pre-processing measure and the product of the decomposition components as input for the prediction. However, finding an appropriate number of components or modes is challenging. Recently, artificial intelligence (AI)-based approaches were employed to many applications with success (e.g., Computer Vision (CV) [14], Natural Language Processing (NLP) [15], and Chess Playing [16]).