摘要
为提高光伏发电系统功率超短期预测的准确性,提出一种基于EEMD-LSTM的光伏电站超短期预测模型。该模型选取某50 MW光伏电站2017年功率数据作为样本,根据天气因素分类指标将天气情况分为非突变天气和突变天气两大类,利用EEMD将分类天气的历史功率数据分解为IMF1~IMF5和剩余分量,计算各个分量与原始数据之间的相关性并将强相关的分量送入LSTM神经网络,叠加各子分量结果得到最终的光伏功率预测结果,同步搭建BP、SVM、KNN和LSTM模型与所提模型进行误差对比。结果表明:天气因素对光伏输出功率有较大影响;单一模型对功率波动较大的突变天气进行预测时会产生较大误差;功率数据经过EEMD分解,可充分提取细节特征,使得EEMD-LSTM耦合模型较LSTM模型在e_(RMSE)、e_(MAPE)、e_(TIC)上分别提升21.23%、11.92%、25.67%。所提模型可有效提高光伏功率超短期预测的准确度,满足光伏发电系统超短期预测的要求。
An ultra-short-term power prediction model of photovoltaic power station based on EEMD-LSTM is proposed in order to improve the accuracy of ultra-short-term power prediction of photovoltaic power generation system. By taking the power data of a 50 MW photovoltaic power station in 2017 as a sample, the weather conditions are divided into two categories: non-abrupt weather and abrupt weather according to the classification index of weather factors. EEMD is used to decompose the historical power data of classified weather into IMF1-IMF5 and residual components. The correlation between each component and the original data is calculated, and the strongly correlated components are sent to LSTM neural network. The final photovoltaic power prediction results are obtained by superimposing the results of each sub-component. BP,SVM, KNN and LSTM models are built synchronously to compare the errors with the proposed models. The results show that weather factors have great influence on photovoltaic output power;When a single model predicts abrupt weather with large power fluctuation, it would produce large errors. After EEMD decomposition of power data, detail features can be fully extracted, which makes EEMD-LSTM coupling model improve by 21.23%, 11.92% and 25.67% on e_(RMSE), e_(MAPE) and e_(TIC) respectively compared with LSTM model. The proposed model effectively improves the accuracy of ultra-short-term prediction of PV power, and meets the requirements of ultra-short-term prediction of PV power generation system.
作者
卢忠山
袁建华
LU Zhongshan;YUAN Jianhua(College of Electrical and New Energy,China Three Gorges University,Yichang 443002,China)
出处
《中国测试》
CAS
北大核心
2022年第12期125-132,共8页
China Measurement & Test
基金
国家自然科学基金项目(61603212)。
关键词
光伏发电
超短期
功率预测
模态分解
photovoltaic power generation
super short term
power prediction
mode decomposition
