ANNSTLF-Artificial Neural Network Short-Term Load Forecaster generation three

Author

Khotanzad, A. ; Afkhami-Rohani, Reza ; Maratukulam, Dominic

Author_Institution

Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA

Volume

13

Issue

4

fYear

1998

fDate

11/1/1998 12:00:00 AM

Firstpage

1413

Lastpage

1422

Abstract

This paper describes the third generation of an hourly short-term load forecasting system known as ANNSTLF (Artificial Neural Network Short-Term Load Forecaster). This forecaster has received wide acceptance by the electric utility industry and is being used by 35 utilities across the US and Canada. The third generation architecture is substantially changed from the previous generation. It includes only two ANN forecasters, one predicts the base load and the other forecasts the change in load. The final forecast is computed by adaptive combination of these two forecasts. The effect of humidity and wind speed are considered through a linear transformation of temperature. A novel weighted interpolation scheme is developed for forecasting of holiday loads, giving improved accuracy. The holiday peak load is first estimated and then the ANNSTLF forecast is re-shaped with the new peak forecast. The performance on data from ten different utilities is reported and compared to the previous generation

Keywords

interpolation; load forecasting; neural nets; power system analysis computing; ANN forecasters; ANNSTLF; Artificial Neural Network Short-Term Load Forecaster; adaptive neural network; base load prediction; change in load forecasting; holiday loads forecasting; humidity effect; weighted interpolation scheme; wind speed effect; Artificial neural networks; Computer architecture; Humidity; Interpolation; Load forecasting; Neural networks; Power industry; Temperature; Wind forecasting; Wind speed;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.736285

Filename

736285