Composite modeling for adaptive short-term load forecasting

Author

Park, J.H. ; Park, Y.M. ; Lee, K.Y.

Author_Institution

Dept. of Electr. Eng., Pusan Nat. Univ., South Korea

Volume

6

Issue

2

fYear

1991

fDate

5/1/1991 12:00:00 AM

Firstpage

450

Lastpage

457

Abstract

A composite load model is developed for predicting hourly electric loads 1-24 h ahead. The load model is composed of three components: the nominal load, the type load, and the residual load. The nominal load is modeled in such a way that the Kalman filter can be used, and the parameters of the model are adapted by the exponentially weighted recursive-least-squares method. The type load component is extracted for weekend load prediction and updated by an exponential smoothing method. The residual load is predicted by the autoregressive model, and the parameters of the model are estimated using the recursive-least-squares method. Test results are presented using utility data for two different years

Keywords

Kalman filters; least squares approximations; load forecasting; Kalman filter; adaptive short-term load forecasting; autoregressive model; composite load model; exponentially weighted recursive-least-squares method; nominal load; residual load; type load; weekend load prediction; Control systems; Economic forecasting; Least squares methods; Load forecasting; Load modeling; Power generation economics; Power system modeling; Power system planning; Predictive models; Weather forecasting;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.76686

Filename

76686