Title : ( Modeling and Forecasting Short-term Electricity Load based on Multi Adaptive Neural-Fuzzy Inference System by Using Temperature )
Authors: zohreh soozanchi-k , mahdi yaghobi , Mohammad Reza Akbarzadeh Totonchi , maryam habibipour ,
Full TextAbstract
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.
Keywords
, Load forecasting, Lyapunov exponent, Multi ANFIS
@inproceedings{paperid:1019475,
author = {Zohreh Soozanchi-k and Mahdi Yaghobi and Akbarzadeh Totonchi, Mohammad Reza and Maryam Habibipour},
title = {Modeling and Forecasting Short-term Electricity Load based on Multi Adaptive Neural-Fuzzy Inference System by Using Temperature},
booktitle = {2010 2nd International Conference on Signal Processing Systems-ICSPS},
year = {2010},
location = {IRAN},
keywords = {Load forecasting; Lyapunov exponent; Multi
ANFIS},
}
%0 Conference Proceedings
%T Modeling and Forecasting Short-term Electricity Load based on Multi Adaptive Neural-Fuzzy Inference System by Using Temperature
%A Zohreh Soozanchi-k
%A Mahdi Yaghobi
%A Akbarzadeh Totonchi, Mohammad Reza
%A Maryam Habibipour
%J 2010 2nd International Conference on Signal Processing Systems-ICSPS
%D 2010
