Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function

Abstract

Short-term wind power forecasting is a challenging issue in renewable power generation and distribution due to inevitable intermittency, complex fluctuation and high volatility of wind energy sources. Deep learning methods, especially recurrent neural networks (RNN), have superior performance among the existing methods. This paper proposes a stacked recurrent neural network (SRNN) with parametric sine activation function (PSAF) algorithm for wind power forecasting, whose parametrization can be tuned adaptively and iteratively during prediction. More precisely, firstly, nonlinear difference equation modeling of SRNN-PSAF is contrived, together with back-propagation training parametrization; secondly, sensitivity analysis of the proposed model with respect to forecasting accuracy is conducted, which provides design guidance in practical situations; thirdly, the proposed model is applied to short-term wind power forecasting based on the national renewable energy laboratory (NREL) wind power data; finally, simulation results are collected to illustrate high capability in retrieving manifold features in wind power sources.