基于EEMD-GABP的某地区短期负荷预测研究

电力系统负荷是具有典型周期性和随机性特点的非线性、非平稳时间序列的负荷系统。为了降低负荷序列的非线性,提高预测精度,提出了集总经验模态分解法(EEMD)和基于改进人工神经网络(GABP)的短期负荷预测法。运用EEMD将负荷序列分解成若干不同频率的平稳分量,突出原负荷数据局部特征,解决了经验模态分解法(EMD)中分类模糊问题,同时利用GABP网络进行预测,解决了BP容易陷入局部最优解的问题,选择合适的参数对各分量构造不同的EEMD-GABP预测模型,引入气象因子对各分量分别预测,重构后得到最终预测值。算例表明,基于EMD-GABP预测模型的负荷量预测精度高于差分整合移动自回归移动(ARIMA)模型、支持向量机(SVM)模型等传统模型,稳定性更强。

Short-term load forecasting in a certain area based on EEMD-GABP

Power system load is a nonlinear, non-stationary time series of load with typical periodicity and randomness.In order to reduce the nonlinearity of the load sequence and improve the prediction accuracy, a lumped empirical mode decomposition method (EEMD)and a modified artificial neural network (GABP)short-term load forecasting method are proposed.Using EEMD to decompose the load sequence into several stationary components of different frequencies, highlighting the local characteristics of the original load data, solving the classification fuzzy problem in EMD decomposition, and using GABP network to predict, solving the problem that BP is easy to fall into the local optimal solution.The appropriate parameters are used to construct different EEMD-GABP prediction models for each component, and the meteorological factors are introduced to predict each component separately, and the final predicted value is obtained after reconstruction.The example shows that the accuracy prediction high stability of load based on EMD-GABP prediction model is higher than that of traditional models such as ARIMA model and SVM model.