Short-term load forecasting is important for electricity load planning and dispatches the loading of generating units in order to meet the electricity system demand. The precision of the load forecasting is related to electricity companyȁ9;s economic. This paper presents a approach named an autoregressive moving average (ARMA) cooperate with BP Artificial Neural Network (BPNN) approach, which can combine the linear component and nonlinear component at the same time. the experiment result shows that the MAPE of this method is 0.92%, and MSE is 17.07, compared to single ARMAȁ9;s MAPE 2.08% and MSE 47.65 or BPNNȁ9;s MAPE 2.63% and MSE 56.91, this method is outperform the single ARMA and BPNN forecast method.
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机译：短期负荷预测对于电力负荷计划很重要，它可以调度发电机组的负荷以满足电力系统的需求。负荷预测的准确性与电力公司的经济性有关。本文提出了一种与BP人工神经网络（BPNN）方法配合使用的自回归移动平均（ARMA）方法，该方法可以同时组合线性分量和非线性分量。实验结果表明，与单个ARMAȁ9; s MAPE 2.08％和MSE 47.65或BPNNȁ9; s MAPE 2.63％和MSE 56.91相比，该方法的MAPE为0.92％，MSE为17.07，优于单个ARMA和BPNN预测方法。
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