针对光伏发电系统输出功率的不稳定现象，为提高光伏电能在并网运行时对其功率的调节能力，在对气象条件进行量化分析的基础上，提出了基于LMD-EE-ESN算法在迭代误差修正下实现光伏发电功率短期预测的方法。首先，通过对气象条件的模糊聚类处理，以晴朗、雨天或雪天、多云和多变天气四种典型天气下的光伏发电功率曲线为参考，分别进行局部均值分解(Local mean decomposition，LMD)，并以各自的能量熵(Energy entropy，EE)作为气象特征。其次，利用LMD算法对历史发电功率序列分解，以回声状态网络(Echo state network，ESN)预测算法结合气象特征实现功率曲线的分级预测。最后，将迭代误差理论用于功率预测结果的修正。分析光伏发电系统历史数据，结果表明，该方法用于光伏输出功率短期预测，可避免气象条件的影响，且在分级预测和迭代误差修正的情况下，可提高功率预测的精准度。
Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction
Considering the instability of the output power of photovoltaic(PV) generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD) was carried out respectively,and their energy entropy (EE) was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN) prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.
photovoltaic(PV) power generation systemshort-term forecastlocal mean decomposition(LMD)energy entropy(EE)echo state network(ESN)
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