基于数据挖掘技术的负荷预测模型

为有效选取预测变量和训练模式、提高预测精度，提出了一个基于数据挖掘技术的负荷预测模型．该模型首先利用粗集理论和遗传算法选取与负荷相关的预测变量，再选取与预测日相似的训练模式，最后用神经网络对负荷进行预测．实际运行结果表明将该模型应用于电力系统负荷预测是可行的，其与传统的神经网络预测模型相比具有更高的预测精度.

Load forecasting model based on data mining technique

In order to efficiently improve the prediction accuracy by selecting input variables and the training pattern, a load forecasting model based on data mining technique is presented. The model consists of three stages: firstly, the rough set theory and the genetic algorithm are applied to find relevant factors to the load; secondly, the active selection of the training pattern is carried out; lastly, the artificial neural network is used to predict load. Testing results on a real power system show that the proposed model is promising for load forecasting and is more accurate than the traditional one.