A hybrid model based on Discrete wavelet prediction (DWT), Bidirectional recurrent neural networks to wind speed prediction

Abstract

Abstract Wind speed is the main driver of wind power output, but its inherent fluctuations and deviations present significant challenges for power system security and power quality. Accurate short-term wind power forecasting is necessary to ensure the stability and integration of wind energy into the grid. Non-stationarity is a major challenge in analyzing wind speed data, and change-point detection are essential for optimal resource allocation. This paper addresses the issue of short-term wind power forecasting for stable and effective wind energy system operation. To predict non-stationary data and detect change points, non-stationary data must first be transformed into stationary data. Discrete wavelet transformation (DWT) is used to decompose wind speed traces into low- and high-frequency components for more accurate predictions using deep learning algorithms. The proposed approach uses a Gated Recurrent Unit (GRU) network, which has a concise network structure and requires less computational load, making it suitable for quickly predicting short-term and long-term dependencies in wind speed data. Experiments demonstrate that the proposed method outperforms other cutting-edge methods in terms of prediction accuracy.