Paper Info
Title
Fuzzy Modeling to Forecast an Electric Load Time Series
Abstract
This paper tests and compares two types of modelling to predict the same time series. A time series of electric load was observed and, as a case study, we opted for the metropolitan region of Bahia State. The combination of three exogenous variables were attempted in each model. The exogenous variables are: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. According to the evaluation of the attempts, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the attempts also indicated different exogenous variables for each forecast model.
Year
DOI
Venue
2015
10.1016/j.procs.2015.07.089
Procedia Computer Science
Keywords
Field
DocType
Forecast,Time Series,Electric Load,SARIMAX,Fuzzy Inference System
Data mining,Time series,Electrical load,Computational intelligence,Linear model,Computer science,Electric power distribution,Fuzzy logic,Artificial intelligence,Forecast error,Machine learning,Fuzzy inference system
Conference
Volume
ISSN
Citations 
55
1877-0509
1
PageRank 
References 
Authors
0.36
2
3
Name
Order
Citations
PageRank
Cesar Machado Pereira110.36
Nival N. Almeida211.38
Maria Luiza F. Velloso3125.93