A model based on decomposition and bidirectional long short-term memory network for short-term wind speed prediction

Abstract

One of the fundamental aspects essential for ensuring the steadiness of wind power generation and the management of power systems is the accurate forecast of wind speed. We propose a short-term wind speed prediction model based on decomposition and bidirectional long short-term memory network. Firstly, the short-term wind speed is input into complete ensemble empirical mode decomposition of adaptive noise processing, which decomposes it into components with different local characteristic information to decrease the complexity of the wind speed pattern. Then, the bidirectional long short-term memory network with the attention mechanism is fitted with the decomposed data, and the particle swarm optimization algorithm is selected to optimize the hyper-parameters of bidirectional long short-term memory network to reduce the errors in modeling process. To derive the final prediction results, the forecasted values of each model output are added. The experimental results of two real short-term wind speed datasets verify that the designed approach has high accuracy in short-term wind speed forecasting, and its prediction values are better than other comparison models.