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

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 forecas...

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Bibliographic Details
Main Authors: Soozanchi-K, Zohreh, Yaghobi, Mahdi, Akbarzadeh-T, Mohammad-R, Habibipour, Maryam
Format: Conference Proceeding
Language:English
Subjects:
Adaptation model
Artificial neural networks
Forecasting
Load forecasting
Load modeling
Lyapunov exponent
Multi ANFIS
Predictive models
Time series analysis
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Summary: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.
DOI:10.1109/ICSPS.2010.5555848