NDLI: Modeling and forecasting short-term electricity load based on multi adaptive neural-fuzzy inference system by using temperature

Content Provider	IEEE Xplore Digital Library
Author	Soozanchi-K, Z. Yaghobi, M. Akbarzadeh-T, M.-R. Habibipour, M.
Copyright Year	2010
Description	Author affiliation: Department of Artificial Intelligence, Islamic Azad University, Mashhad Branch, Iran (Soozanchi-K, Z.) \|\| Sama Organization, (affiliated with Islamic, Azad University)- Mashhad, Branch, Iran (Habibipour, M.) \|\| Center for Applied Research on Soft Computing and Intelligent Systems, Mashhad, Iran (Akbarzadeh-T, M.-R.) \|\| Islamic Azad University, Mashhad Branch, Iran (Yaghobi, M.)
Abstract	In this paper, the use of Adaptive Neural-Fuzzy Inference System (ANFIS) to study the design of Short-Term Load Forecasting (STLF) 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.
File Size	294684
File Format	PDF
ISBN	9781424468928
e-ISBN	9781424468935
DOI	10.1109/ICSPS.2010.5555848
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Time series analysis Adaptation model Artificial neural networks Lyapunov exponent Predictive models Forecasting Load modeling Multi ANFIS
Content Type	Text
Resource Type	Article

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