为了提高负荷预测的精度，在负荷预测模型的建立过程中往往选取一些相关影响因素。然而此种情况往往将一些不重要的因素也考虑进来，增加了负荷建模的难度，有时甚至出现模型不好解释的现象。为了避免这种情况，提出了首先用岭估计法筛选所有选出的自变量集合，剔除那些对负荷影响不显著的因素，然后用剩余的自变量集合通过偏最小二乘回归法建模。通过实例应用，证明了该方法的正确性和有效性。

In order to improve the precision of the load forecasting, some related factors are chosen in the process of establishing a load forecasting model. However, some factors that are not very important are taken into consideration, which increases the difficulty of establishing a load forecasting model. Even sometimes it is difficult to explain the model established. In order to avoid this kind of situation, the RE(ridge estimate) method is firstly used to select the independent variable set, and to exclude those factors which are not significant. Then, the PLSR(partial least squares regression) method is applied to establish the model based on the rest of the independent variables set. Through the practical example, it is shown that the method proposed in this paper is correct and effective.