A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm

Abstract

Accurate wind speed forecasting is capable of increasing the stability of wind power system. Notably, there are numerous factors affecting wind speed, thus causing wind speed forecasting to be difficult. To address the above-mentioned challenge, a novel hybrid model integrating genetic algorithm (GA), variational mode decomposition (VMD), improved dung beetle optimization algorithm (IDBO), and Bidirectional long short-term memory network based on attention mechanism (BiLSTM-A) is proposed in this study to achieve satisfactory forecasting performance. In the proposed model, GA is adopted to optimize the VMD to eliminate noise and extract original series attributes. And the IDBO is adopted for hyperparameters selection for the BiLSTM-A. The proposed GA-VMD-IDBO-BiLSTM-A is compared with nine established comparable models, with the aim of verifying its forecasting performance. A series of experiments on four 1-hour real wind series in Stratford are performed to assess the performance of the model. The MAPE of the four datasets forecasting results reached 1.4%, 2.4%, 3.5%, 2.4%. As indicated by the experimental results, GA-VMD can better process the data and improve the forecasting accuracy. IDBO can optimize the parameters of BiLSTM model and improve the forecasting performance. The dual-optimization wind speed forecasting model can obtain high accuracy and strong stability.