Modeling and Forecasting Short-term Electricity Load based on Multi Adaptive Neural-Fuzzy Inference System by Using Temperature

摘要：

In this paper, the use of Adaptive Neural-Fuzzy Inference System (ANFIS) to study the design of Short-Term Load Forecasting (STLD systems for the east of Iran was explored. While reviewing the probability of chaos and predictability of electricity load curve by Lyapunov exponent, this paper forecasts consumed load by using multi ANFIS. Entries of the presented model are into the multi ANFIS including the date of the day, temperature maximum and minimum, climate condition and the previous days consumed load and its exit is forecasting of power load consumption of every season. The results show that temperature has an important role in load forecast.

关键词： Load forecasting Lyapunov exponent Multi ANFIS

作者: Zohreh Souzanchi-K Mahdi Yaghobi Mohammad-R Akbarzadeh-T Maryam Habibipour

作者单位: Department of Artificial Intelligence Islamic Azad University,Mashhad Branch, Iran Islamic Azad University,Mashhad Branch, Iran Center for Applied Research on Soft Computing and Intelligent Systems Mashhad, Iran Sama Organization (affiliated with Islamic Azad University)- MashhadBranch, Iran

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 1699-1703

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）