基于概念格的神经网络日最大负荷预测输入参数选择

针对电力系统中影响负荷预测精度的众多因素如何选择问题,提出一种概念格属性约简算法,采用该算法挖掘出与待预测负荷量相关性较大的各属性作为神经网络预测模型的输入参数,降低了输入参数规模,确保了负荷预测模型输入参数的合理性,解决了神经网络模型输入参数的确定问题.通过对重庆市某区实际日最大负荷数据的计算分析,结果表明该算法提高...

Input Parameters Selection in Neural Network Load Forecasting Mode Based on Concept Lattice

There are many factors to affect the accuracy of load forecasting. The problem is how to choose the factors. To solve the problem, an attribute reduction algorithm of concept lattice was introduced. We chose a property parameter that has good relativity to forecasting load as the input parameter of the forecasting model of neural network. It reduces the scope of the inputparameters, ensures the rationality of input parameters of the forecasting model, and solves the problem how to determine the input parameters of the neural network model. The actual data of the maximum load in some place of Chongqing City was calculated. The results show the prediction accuracy of neural network model is improved with such a method, and reduction algorithm is reasonable and effective.