CAJ | 학술논문

以河南某地区电网2009年和2010年的电力负荷数据为基础,设计了一个由输入层、隐含层和输出层组成的三层 BP 网络模型,利用神经网络高度非线性建模能力,在不考虑温度影响情况下,采用 BP 神经网络对该地区短期电力负荷进行预测.探讨了负荷预测模型分类模式,对应用于实际的 BP 神经网络算法进行了具体处理.结果表明,基于 BP 神经元网络的短期电力负荷预测方法具有精度高的特点,取得了均方根误差小于3.02%的精度.
이하남모지구전망2009년화2010년적전력부하수거위기출,설계료일개유수입층、은함층화수출층조성적삼층 BP 망락모형,이용신경망락고도비선성건모능력,재불고필온도영향정황하,채용 BP 신경망락대해지구단기전력부하진행예측.탐토료부하예측모형분류모식,대응용우실제적 BP 신경망락산법진행료구체처리.결과표명,기우 BP 신경원망락적단기전력부하예측방법구유정도고적특점,취득료균방근오차소우3.02%적정도.
@@@@Based on the power load data in 2009 and 2010 of a certain area power grid in Henan,a three layer BP neural network model consisting of input layer,hidden layer and output layer is designed. Using the nonlinear modeling ability of neural network,the short-term power load in the area without considering the temperature influence is forecasted. The classification mode of the load forecasting models is discussed and the actual BP neural network algorithm is handled. The results show that the neural network for short-term load forecasting method based on BP has high accuracy,the root-mean-square error is less than 3.02% .