基于小波包分解与最小二乘支持向量机的用户侧净负荷预测
摘要：随着分布式可再生能源在用户侧逐步接入，电表监测得到的用户净负荷曲线形态相对于原有实际负荷曲线更加不稳定，因而极大降低了用户的净负荷预测精度。针对此问题，提出基于小波包分解(wavelet packet decomposition，WPD)与最小二乘支持向量机(least squares support vector machine，LSSVM)的用户侧净负荷预测方法，通过对用户净负荷时序数据作小波包分解，得到信号特征更为明显的高频分量与低频趋势部分，筛选剔除波动性大、噪声信号多的高频细节分量。同时考虑气象因素，利用最小二乘支持向量机对小样本非线性信号的训练效率高、泛化能力强的特点，采用其模型对其余包含更多有效负荷数据信息的低频分量分别进行预测重构，叠加得到最终的净负荷预测值。通过对可再生能源高度渗透的某地区用户实际净负荷数据进行实例分析，结果表明所提预测方法在此物理场景下相比于传统预测方法有更高的预测精度。
Abstract:Along with the gradual grid-connection of distributed renewable energy at the user side, relative to original actual load curve the form of users’ net load curve monitored by electric meter becomes more unstable, so that the predicted accuracy of users’ net load is extremely decreased. For this reason, based on the wavelet packet decomposition (abbr. WPD) and least squares support vector machine (abbr. LSSVM) a method to predict user side net load was proposed. Through the WPD of users’ net load time series data the high frequency components and the low frequency trend with more evident signal features were obtained, and the high-frequency detail components, which evidently fluctuated and contained many noise signals, were screened and rejected. Meanwhile, considering meteorological factors and such characteristics of LSSVM as high training efficiency and strong generalization ability, the trained LSSVM model was used to respectively predict and reconstruct other low-frequency components containing more effective load data information, and then superposed them to obtain final predicted value of net load. Results of instance analysis on users’ actual net load data of a certain region with high penetration of renewable energy show that under such a physical scene a higher load prediction accuracy can be obtained than by traditional prediction method.
标题：基于小波包分解与最小二乘支持向量机的用户侧净负荷预测
英文标题：User-side Net Load Forecasting Based on Wavelet Packet Decomposition and Least Squares Support Vector Machine
作者：吴浩, 齐放, 张曦, 刘友波, 向月, 刘俊勇,
关键词：净负荷预测, 可再生能源, 小波包分解, 最小二乘支持向量机,
发表日期：2023-04-10
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