Least Squares Estimation of Polynomial AR Models to Forecast Short Term Power Demand

The paper considers estimation problems of polynomial AR models based on least squares methods. The polynomial AR models are a kind of nonlinear AR models. System identification problems of the polynomial AR models are reduced to least squares problems using multiple data if the nonlinear functions...

Full description

Saved in:
Bibliographic Details
Published in2024 14th Asian Control Conference (ASCC) pp. 1420 - 1423
Main Authors Azuma, Takehito, Yokota, Sodai
Format Conference Proceeding
LanguageEnglish
Published Asian Control Association 05.07.2024
Subjects
Data models
electrical power demand forecasting
electrical power systems
Estimation
least squares methods
Mathematical models
nonlinear AR models
polynomial AR models
Polynomials
Power demand
Power measurement
Predictive models
system identification
Online AccessGet full text

Cover

More Information
Summary:The paper considers estimation problems of polynomial AR models based on least squares methods. The polynomial AR models are a kind of nonlinear AR models. System identification problems of the polynomial AR models are reduced to least squares problems using multiple data if the nonlinear functions are assumed to be polynomial. The estimated polynomial AR models are used as electrical power demand forecasting. A numerical example is shown to demonstrate the effectiveness of the proposed method about short term demand forecasting in Tokyo.
ISSN:2770-8373