作　　者： ; ;

机构地区： 湖南大学电气与信息工程学院

出　　处： 《电力系统及其自动化学报》 2007年第4期55-58,共4页

摘　　要： 每个电力负荷预测模型各有特色,又有其局限性。针对单个模型的局限性,提出一个基于条件熵的组合预测模型,以便结合各个模型的优点,克服其不足。该模型用条件熵度量各个电力负荷预测模型的精度,条件熵越小,表明预测精度越高。然后对条件熵进行模糊评判,对条件熵小的模型赋予较大的权重,条件熵大的模型赋予较小的权重,以此建立一个电力负荷组合预测模型。实例应用表明,就6个误差指标而言,该文提出的组合模型可以进一步提高预测精度。 Various load forecasting models all have their own characteristics, but they are limited in some aspects as well. In order to combine the advantages of various models and overcome their disadvantages, this paper proposes a combination model for load forecasting based on conditional entropy. The proposed model measures the accuracy of each model by conditional entropy. The smaller value of conditional entropy indicates the more accurate model. Then, by fuzzy evaluation of the conditional entropy of each model, the model with smaller conditional entropy is assigned bigger weight, vice versa. Experimental results show that the proposed model can further improve the accuracy of load forecasting.

关 键 词： 条件熵 模糊评判 组合预测

领　　域： [电气工程]