基于局域波法和LSSVM的短期负荷预测

负荷预测应该充分考虑负荷曲线本身的非平稳性和非线性,局域波通过分解负荷能得到一系列平稳化分量,但是仍存在端点效应。为了降低负荷序列的非线性来提高预测精度,提出在局域波分解的基础上引入四中点估计法来减弱端点效应带来的误差,然后在分析各分量特征和最小二乘支持向量机的基础上,对各分量建立最优参数下的DEMD-LSSVM预测模型,重构得到预测值。最后针对某电网的实际电力负荷分别进行普通休息日和特殊节假日的实例仿真,并与DEMD-SVM预测模型进行对比分析,验证DEMD-LSSVM模型的实用性和有效性。

Short-term Load Forecasting Based on Local Wave Method and LSSVM

Load forecasting should fully consider nonstationary and nonlinear of the load. Local wave can decompose the load to get a series of stationary components, but the problem of end effects still exists. In order to reduce the nonlinear of load sequence to improve the accuracy of load forecasting, this paper introduces the four-midpoint estimation method to reduce the error on end effect based on the decomposition of local wave method. According to the characteristics of each component and least squares support vector machine, the paper uses optimal parameters to establish the DEMD-LSSVM prediction model for each component, then reconstructing to obtain the prediction results. Finally the paper takes actual load of power grid to make simulations on normal rest days and special holiday, compared with DEMD-SVM prediction model, to prove the validity and practicability of DEMD-LSSVM prediction model.