电子科技2024，Vol.37Issue(4)：30-37,8.DOI:10.16180/j.cnki.issn1007-7820.2024.04.005
基于CEEMD-ITSA-BiLSTM组合模型的短期负荷预测
Short-Term Load Forecasting Based on CEEMD-ITSA-BiLSTM Combined Model
摘要
Abstract
Accurate short-term load forecasting of power system is helpful to flexible planning of system re-sources,reasonable scheduling of units,and improvement of system operation efficiency.In view of the accuracy of load forecasting,this study proposes a short-term load forecasting model based on CEEMD-ITSA-BiLSTM(Com-plete Ensemble Empirical Mode Decomposition-Improved Tunicate Swarm Algorithm-Bidirectional Long Short-Term Memory).CEEMD decomposition is carried out on the time series load data to obtain several stable IMF(In-trinsic Mode Function),and BiLSTM modeling and prediction are carried out for each IMF.To improve the accuracy of BiLSTM,ITSA algorithm is used to optimize the parameters of the super parameters such as the number of hidden layer nodes,learning rate and training times of BiLSTM,and CEEMD-ITSA-BiLSTM load forecasting model is es-tablished.The simulation experiment is conducted with the actual load data,and the single BiLSTM model and the BiLSTM model optimized by different algorithms are compared.The results show that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error)and MAPE(Mean Absolute Percentage Error)error indexes of CEEMD-ITSA-BiLSTM model are increased by 48.54%,51.32%and 44.78%,respectively when compared with the BiL-STM model,and are significantly lower than other comparison models.关键词
短期负荷预测/预测精度/完全集成经验模态分解/本征模函数/被囊群算法/参数寻优/双向长短期记忆神经网络/误差指标Key words
short term load forecasting/prediction accuracy/CEEMD/IMF/TSA/parameter optimization/BiLSTM neural network/error index分类
信息技术与安全科学引用本文复制引用
高典,张菁..基于CEEMD-ITSA-BiLSTM组合模型的短期负荷预测[J].电子科技,2024,37(4):30-37,8.基金项目
国家自然科学基金(61902237)National Natural Science Foundation of China(61902237) （61902237）
