Multilevel probabilistic wind power forecasting using an adaptive Informer network

Abstract

Effective and feasible wind power forecasting is critical to the resource allocation and safe control of the power system. Nevertheless, the volatility and randomness of wind speed changing leads to deviations in actual wind power output. Therefore, a multilevel probabilistic wind power forecasting strategy using an adaptive Informer network is developed. To separate the long-term trend and periodic fluctuation of the raw series, wind power is firstly decomposed into equal-length sequences of multilevel frequencies through the maximum discrete overlapping wavelet transform (MODWT). Simultaneously, a piecewise adaptive loss function and an activation function for large range are considered in a novel Informer network, and the inherent structure and nonlinear features at each frequency are extracted with two layers of encoders and one layer of decoders. Moreover, the ensemble batch prediction intervals (EnbPI) are exploited to extend the deterministic forecasting to probabilistic information. Ultimately, a historical dataset is applied from an offshore wind power system in Belgium to verify that the forecasting performance, and quantitative analysis shows that the model achieves a mean absolute error of 2.5 % and a root mean squared error of 3.8 %. The developed strategy handles the volatility and complexity of wind data, providing reliable support for real wind power plant.