基于SA-LSSVM的电力短期负荷预测

提出融合模拟退火（Simulated annealing，SA）和最小二乘支持向量机（Least Square Support Vector Machine，LSSVM）的电力短期负荷预测方法.由于LSSVM的预测精度依赖于其参数的选择，并且难以选取合适的参数值，因此，参数选择是LSSVM的一个关键问题.为了提高参数选择的质量和效率，采用SA算法进行LSSVM的参数寻优.以某市2010年1月1日至2011年1月7日的电力负荷数据和气象数据进行仿真实验, 实验结果表明该方法具有较高的预测精度.

Power short-term load forecasting based on SA-LSSVM

The paper proposes a power short-term load forecasting method using simulated annealing and least square support vector machine.Because its prediction accuracy is dependent on the choice of its parameters, and it is very difficult to select the appropriate parameter values,therefore parameter selection is a key issue in LSSVM. In order to improve the quality and efficiency of parameter selection, the paper used the SA algorithm to optimize the parameters of LSSVM. The proposed model is applied to the short-term electrical power load forecasting using power load and meteorological data of a city in China from 2010-1-1 to 2011-1-7.The experimental results show that the proposed method has higher prediction accuracy.