摘要
电力负荷观测值由于受到各种因素的影响,正常的负荷数据中夹杂着许多脏数据,严重影响负荷预测的精度。对此提出了由自适应共振网络(ART网络)和超圆神经元网络(CC网络)组合而成的神经网络模型,并应用该模型清洗陕西省某地区的负荷数据,结果表明该模型能较好的完成脏数据的辨识任务,对负荷数据修正后能明显提高负荷预测精度。
The load values of power systems have some ＂dirty data＂ due to the effect of various factors. These ＂dirty data＂ affect the accuracy of load forecasting seriously. This paper puts forward an intelligent model which comprised of adapt resonance network （ ART network） with super circle network （ CC network）. By using this model to clean the load data of a certain area in shaanxi ,the effect shows that the model can recognize dirty data completely, and the revised load data can improve the load forecasting accuraty.
出处
《陕西理工学院学报（自然科学版）》
2008年第1期25-28,共4页
Journal of Shananxi University of Technology:Natural Science Edition
