摘要
为提高风电出力预测精度,提出一种自适应噪声完备集合经验模态分解(CEEMDAN)-贝叶斯优化(BO)-长短期时序网络(LSTNet)对风电机组输出功率进行短期预测。清洗数据,采用CEEMDAN对清洗后的原始功率数据进行分解,得到若干个子序列;将分解得到的子序列输入至LSTNet模型,通过对LSTNet的超参数使用BO算法优化,输出子序列的预测结果;将各序列的预测结果进行叠加重构得到最终预测结果。通过对渭南某风电场机组实测数据进行实例仿真,设置消融分析和对比分析,结果表明文中所提方法相较于其他模型,预测精度得到有效提升。
In order to improve the prediction accuracy of wind power output,an adaptive noise-complete ensemble empirical mode decomposition(CEEMDAN)-Bayesian optimization(BO)-long and short-term time-series network(LSTNet)is proposed to predict the output power of wind turbines in the short term.Firstly,the data are cleaned,and then,CEEMDAN is used to decompose the original power data after cleaning,and several sub-sequences are obtained.The decomposed subsequences are input into the LSTNet model,the LSTNet hyperparameters are optimized by using BO algorithm,and output the prediction results of the subsequences.Finally,the prediction results of each sequence are superimposed and reconstructed to obtain the final prediction results.Through the example simulation of the measured data of a wind farm unit in Weinan,ablation analysis and comparative analysis are set up.The results show that compared with other models,the prediction accuracy of the proposed method is effectively improved.
作者
庞博文
丁月明
杜善慧
谭亲跃
康定毅
尚文强
Pang Bowen;Ding Yueming;Du Shanhui;Tan Qinyue;Kang Dingyi;Shang Wenqiang(College of Water Conservancy and Civil Engineering,Northwest A&F University,Xianyang 712100,Shaanxi,China;Rizhao Power Supply Company,State Grid Shandong Electric Power Co.,Ltd.,Rizhao 276800,Shandong,China)
出处
《电测与仪表》
北大核心
2023年第9期109-116,170,共9页
Electrical Measurement & Instrumentation
基金
国家电网公司总部科技项目(5400-202216167A-1-1-ZN)。
关键词
风电出力
短期预测
长短期时序网络
自适应噪声完备集合经验模态分解
贝叶斯优化
wind power output
short-term forecasting
long and short-term time-series network
adaptive complete empirical mode decomposition
Bayesian optimization
