基于完备经验模态分解与深度学习的短期风电功率预测

Short-Term Wind Power Forecasting Based on CEEMD and Deep Learning Model

摘要: 根据各因素对风电场输出功率的影响，建立了基于CEEMD-GRU短期风电场输出功率的预测模型。通过与单模型BPNN、SVM、GRU和组合模型EMD-GRU、CEEMD-BP的预测结果进行对比，最终通过误差评价指标得出对风电功率预测结果精度较高的组合模型，以此来提高短期风功率预测精度

Abstract: According to the influence of various factors on the output power of wind farms, a short-term wind farm output power prediction model based on CEEMD-GRU is established. By comparing with the prediction results of the single model BPNN, SVM, GRU and the combined model EMD-GRU, CEEMD- BP, the combined model with higher accuracy for the wind power prediction results is obtained through the error evaluation indicators, so as to improve the short-term wind power prediction accuracy.