Short-Term Load Forecasting Based on RS-ART

摘要：

This paper presents a short-term electric load forecasting method based on Autoregressive Tree Algorithm and Rough Set Theory. Firstly, Rough Set Theory was used to reduce the testing properties of Autoregressive Tree. It can optimize the Autoregressive Tree Algorithm. Then, Autoregressive Tree Model of Short-term electric load forecasting is set up. Using Rough Set The-ory, the attributes will be reduced off; whose dependence is zero, through knowledge reduction method. It not only avoids the complexity and long train-ing time of the model, but also considers various factors comprehensively. At the same time, this algorithm has improved the prediction rate greatly by using automatic Data Mining Algorithms. Practical examples show that it can improve the load forecast accuracy effectively, and reduce the prediction time.

关键词： Rough Set (RS) Autoregressive Tree Algorithm (ART) Short-Term Electric Load Forecasting Data Mining

作者: Tao Yang Feng Zhang Qingji Li Ping Yang

作者单位: College of Information and Electrical Engineering, Shenyang Agriculture University,Shenyang 110061, China

会议类型: 国际会议

会议名称: The 4th IFIP International on Computer and Computing Technologies in Agriculture and the 4th Symposium on Development of Rural Information(第四届国际计算机及计算机技术在农业中的应用研讨会暨第四届中国农业信息化发展论坛 CCTA 2010)

会议地点: 南昌

会议语种:英文

页码: 413-418

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）