DocumentCode :
3653263
Title :
Load forecasting assessment using SARIMA model and fuzzy inductive reasoning
Author :
Néstor González Cabrera;G. Gutiérrez-Alcaraz;Esteban Gil
Author_Institution :
Department of Electromechanical Engineering, Instituto Tecnoló
fYear :
2013
Firstpage :
561
Lastpage :
565
Abstract :
Accurate load forecasting is critical for power systems planning, control, and operation. Poor forecasting in volatile power markets can have large, detrimental impacts on power system costs and real-time energy acquisition costs from distribution companies. This paper implements and compares two different methodologies for short term load forecasting: a classic statistical model (SARIMA model) and a model based on artificial intelligence (Fuzzy Inductive Reasoning, or FIR, model). A numerical example predicts one week for every methodology and the results are compared for both models.
Keywords :
"Predictive models","Load modeling","Load forecasting","Data models","Computational modeling","Finite impulse response filters","Numerical models"
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2013 IEEE International Conference on
Type :
conf
DOI :
10.1109/IEEM.2013.6962474
Filename :
6962474
Link To Document :
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