控制理论与应用2024，Vol.41Issue(12)：2304-2314,11.DOI:10.7641/CTA.2023.20969
基于相似日与BiLSTM组合的短期电力负荷预测
Short term power load forecasting based on the combination of similar days and BiLSTM
摘要
Abstract
Short term power load has the characteristics of nonlinearity,volatility and many influencing factors.Aiming at the lack of forecasting accuracy caused by the above characteristics,a short-term power load forecasting model based on the combination of similar days and bi directional long short memory neural network(BiLSTM)is proposed.First,the dynamic change mechanism of power load is analyzed,and the similar day and gray correlation analysis methods are introduced to build the load and feature fusion data set;Secondly,the nonlinear and highly fluctuating original load data is decomposed into several relatively stable components by using the variational modal decomposition(VMD)method,and the BiLSTM prediction model is built for each component;Finally,the whale optimization algorithm(WOA)is used to optimize the decomposition parameters and similar days of the model to reduce the inherent error of the model.Based on the actual data of a region in New England,the simulation results show that the MAPE,MAE and RMSE of the proposed model are 0.58％,42 and 78 respectively,which are better than the control model and effectively improve the accuracy of load forecasting.关键词
短期电力负荷预测/相似日/深度学习/鲸鱼优化算法/变分模态分解Key words
short-term power load forecasting/similar day/deep learning/whale optimization algorithm/variational modal decomposition引用本文复制引用
祁宇轩,范俊岩,吴定会,汪晶..基于相似日与BiLSTM组合的短期电力负荷预测[J].控制理论与应用,2024,41(12):2304-2314,11.基金项目
国家重点研发项目(2020YFB1711100,2020YFB1711102)资助.Supported by the National Key Scientific Research Project(2020YFB1711100,2020YFB1711102). （2020YFB1711100,2020YFB1711102）
