Design of a non-linear time series prediction model for daily electricity demand forecasting

2013 ◽  
Vol 7 (3) ◽  
pp. 298 ◽  
Author(s):  
S. Uma ◽  
A. Chitra ◽  
J. Suganthi
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Linear Time ◽  
Time Series Prediction ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Electricity Demand Forecasting ◽  
Non Linear

Analysis of Time Series Models for Brazilian Electricity Demand Forecasting

2021 ◽  
Author(s):  
Carlos Eduardo Velasquez Cabrera ◽  
Matheus Zocatelli ◽  
Fidellis B.G.L. e Estanislau ◽  
Victor Faria
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Time Series Models ◽  
Electricity Demand Forecasting ◽  
Analysis Of Time Series

The Prediction Model of Chaotic Series Based on Support Vector Machine and its Application to Runoff

2011 ◽  
Vol 255-260 ◽  
pp. 3594-3599 ◽  
Author(s):  
Guo Rong Yu ◽  
Ju Rui Yang ◽  
Zi Qiang Xia
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Phase Space ◽  
Prediction Model ◽  
Linear Prediction ◽  
Linear Time ◽  
Surface Flow ◽  
Support Vector ◽  
Good Prediction ◽  
Non Linear

Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series. Applying the Chaos and non-linear time series based on the support vector machine regression principle, this paper proposes a method and its characteristic and the choosing of key parameters to forecast and set up models. According to Phase Space Reconstruction theory carry on reconstruction of Phase Space to monthly surface flow course, have discussed that probed into the non-linear prediction model of time series of Chaos of the support vector machine, application in the monthly surface flow, have introduce it through to the nuclear function of the base in the course of setting up the model of support vector machine, has simplified the course of solving the non-linear problems. The instance indicates that the model can deal with the complicated hydrology data array well, and there is the good prediction precision.

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