A deep-learning wind speed interval forecasting architecture based on modified scaling approach with feature ranking and two-output gated recurrent unit

Wind power is already an indispensable part of the power supply worldwide, for which wind speed forecasting is technically required as essential work to guarantee its safe and economical generation. To date, the majority of wind speed forecasting models only provide deterministic predictions, which, however, fails in accounting for the uncertainty associated with the wind speed that is critical to practice; the current few models attempting to use interval forecasting to address the uncertainty are always plagued by rigorous hypotheses along with the used interval construction approaches and poor interval quality. To fill the gap, a novel interval forecasting architecture has been developed to better capture the uncertainty. It comprises a modified scaling approach, a two-output Gated Recurrent Unit (GRU), and the efficient feature ranking (FR), getting rid of rigorous hypotheses and constructing reliable prediction intervals. The case study based on real wind farm data sheds light on the advantage of the modified scaling approach, as it outperforms many benchmark approaches and conventional scaling approaches by enhancing the coverage width criterion by 4.07% to 97.76% on average. Further embedding the FR and the deep-learning GRU helps in further narrowing the interval width and reducing the interval deviation.