Short-Term Probabilistic Forecasting of Renewable Energy Generation with Direct Multistep-Ahead Strategy and Recurrent Neural Network

With the rapid development of renewable energy generation, probabilistic forecasting has attracted more attention compared to deterministic forecasting. This paper focuses on generating short-term probabilistic forecasting of renewable energy power with quantiles chosen as uncertainty representation. First, in order to avoid the crossing-quantile problem, some constraints associated with quantile-increment series, which are obtained by reformulating the quantile series, are proposed. Then, recurrent neural network is adopted to depict the complex nonlinear relationship between predictors and quantiles, and a reasonable decoder structure is designed to obtain multistep-ahead quantiles prediction directly. Numerical results on a real-world solar power dataset verify the effectiveness of our proposed model, which is capable of providing the high-quality quantiles with less time compared with some advanced benchmarks.