改进神经网络算法的智能电网短期负荷预测

Short Term Load Forecasting Based on Improved Neural Network Algorithm

为了提高短期电力负荷预测的精度,提出了一种考虑实时电价短期符合预测。首先,参考了多方面的因素比如节假日、温度等等建立了模型。其次,主成分分析主要运用了降低维数的原理,而且降低维数后的矩阵也包含了原矩阵的信息。再次,由于神经网络算法在运算过程中容易陷入局部极小点,这样就选择了运用遗传算法对其进行优化,去除其这个缺点。最后,通过Matlab仿真训练得到预测结果。实验说明,该方法利用神经网络的高度非线性的优点以及遗传算法对神经网络进行了优化和PCA降维原理得到最终预测结果。通过实验例证,该方法具有更高的负荷预测精度。

In order to improve the accuracy of short-term power load forecasting, method considering real-time price temperature and so on to build a model. dimension of the principle, and reduce the matrix. Again, because the neural network First of all, a reference to a number of Secondly, the principal component a short-term forecasting factors such as holidays, analysis is used to reduce the dimension of the matrix also contains the information of the original algorithm is easy to fall into the local minimum in the process of operation, so that the use of genetic algorithm to optimize it, remove the shortcomings of its. Finally, the prediction results are obtained by Matlab simulation. Experimental results show that the proposed method is based on the high nonlinearity of neural network and genetic algorithm to optimize the neural network and the PCA dimension reduction theory is used to get the final result. Through experimental examples, the method has higher accuracy of load forecasting.