用于考虑多个相关因素影响的负荷预测时，偏最小二乘法(partial least squares regression，PLSR)通过提取影响负荷的自变量集的主成分，克服了自变量间多重相关性对于负荷建模带来的不利影响，具有对模型解释能力强的优点。但PLSR也有其自身的弱点，如自变量系统中可能存在与因变量无关的正交数据信息，而影响模型的预测精度。基于PLSR算法和BP神经网络的特性，建立了一种PLSR-BP神经网络模型，该模型对原PLSR建模过程中产生的权值和回归系数进行修正，仿真研究结果证明了该方法的正确性和有效性。

Partial least squares regression (PLSR) is used to load forecasting with considering several related factors, and it has a strong ability to explain the forecasting model. Because it selects the principal components of the independent variable set which are related to the load, and it overcomes the negative influence of the multiple relativity between the independent variables on the load modeling. But PLSR also has some weakness, such as there is useless orthogonal information between the independent variables and dependent variables, which may decrease the model’s forecasting accuracy. Based on the characteristics of PLSR and BP neural network, a PLSR-BP neural network was established, which could modified the weight and regression coefficient in original PLSR model. The practical example result shows that this method is correct and effective.