A Regional Wind Power Probabilistic Forecast Method Based on Deep Quantile Regression

Different from individual wind power forecast and regional wind power forecast (RWPF), one of the most significant research articles for alleviating negative influence on power systems aims to estimate the generation of multiple wind farms in the specific region, which is a valuable complement of the wind power forecast. This article proposes a nonparametric probabilistic method for RWPF, a quantile regression neural network (QRNN), enhancing the abilities of nonlinear mapping and massive data dealing. On this basis, the deep quantile regression is proposed to improve the performance of the QRNN. In this approach, the local-connected method is applied to the input layer of the neural network for tackling the challenge of the massive data. A ramp function is designed to avoid multiple quantile curves crossing problem. To improve the model's generalization capability, a smoothing method is applied to the loss function for achieving differentiability everywhere. By properly constructing the model, the approach provides a specific solution for RWPF with the massive input information. The test results on a region with ten wind farms demonstrate the effectiveness of the proposed approach.

Automated summary

This article introduces a nonparametric probabilistic method for regional wind power forecast, which uses quantile regression neural networks (QRNN) and deep quantile regression to handle massive data. The model's performance is enhanced by applying local-connected methods and a ramp function, with test results demonstrating its effectiveness.