基于PCA与改进BP神经网络相结合的电网中长期负荷预测

本文在标准反向传播神经网络的基础上，提出一种结合主成分分析法和改进的误差反向传播神经网络的方法来对电网中长期的电力负荷进行预测。首先利用主成分分析法对电力负荷的影响因素进行特征提取，有效地降低数据样本的维度，消除数据的冗余和线性信息，保留主要成分作为模型的输入数据。然后在标准的神经网络的反向传播环节中引入动量项和陡度因子。两种方法的结合有效地解决了网络收敛速度慢和容易陷入局部最小值的问题。将此方法应用于济源市的中长期电力负荷预测，实验结果表明，基于主成分分析法与改进的反向传播神经网络相结合的方法比常用的标准的反向传播神经网络、基于多变量的时间序列网络及时间序列网络具有更高的计算效率和预测精度，证明提出的预测模型在电力负荷预测中是有效的。

Medium and long-term load forecasting of power grid based on PCA combined with improved BP neural network

Based on the standard back propagation neural network, this paper proposes a method combining principal component analysis and improved error back propagation neural network to predict the long-term power load of the power grid. Firstly, principal component analysis is used to extract the factors affecting the power load, effectively reduce the dimension of the data sample, eliminate the redundancy and linear information of the data, and retain the main components as the input data of the model. The momentum term and the steepness factor are then introduced in the back propagation of the standard neural network. The combination of the two methods effectively solves the problem of slow network convergence and easy to fall into local minimum. Applying this method to medium and long-term electric load forecasting in Jiyuan City, the experimental results show that the method based on principal component analysis combined with improved back-propagation neural network is more common than the standard back-propagation neural network, multivariate-based time series network and time series network have higher computational efficiency and prediction accuracy, which proves that the proposed prediction model is effective in power load forecasting.