Weather sensitive short-term load forecasting using nonfully connected artificial neural network

Author

Chen, Shin-Tzo ; Yu, David C. ; Moghaddamjo, A.R.

Author_Institution

Wisconsin Univ., Milwaukee, WI, USA

Volume

7

Issue

3

fYear

1992

fDate

8/1/1992 12:00:00 AM

Firstpage

1098

Lastpage

1105

Abstract

The authors present an artificial neural network (ANN) model for forecasting weather-sensitive loads. The proposed model is capable of forecasting the hourly loads for an entire week. The model is not fully connected; hence, it has a shorter training time than the fully connected ANN. The proposed model can differentiate between the weekday loads and the weekend loads. The results indicate that this model can achieve greater forecasting accuracy than the traditional statistical model. This ANN model has been implemented on real load data. The average percentage peak error for the test cases was 1.12%

Keywords

load forecasting; neural nets; power system analysis computing; average percentage peak error; hourly loads; nonfully connected artificial neural network; training time; weather sensitive short-term load forecasting; weekday loads; weekend loads; Artificial neural networks; Expert systems; Input variables; Load forecasting; Load modeling; Neurons; Power system modeling; Predictive models; Transfer functions; Weather forecasting;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.207323

Filename

207323