Short Term Power Load Forecasting by Using Neural Models

Author

Chen, Huang-Chi ; Lin, Yi-Ching ; Chen, Yu-Ju ; Chang, Chuo-Yean ; Huang, Huang-Chu ; Hwang, Rey-Chue

Author_Institution

Electr. Eng. Dept., I-Shou Univ., Kaohsiung, Taiwan

fYear

2009

fDate

7-9 Dec. 2009

Firstpage

1212

Lastpage

1215

Abstract

This paper presents the power load forecasting by using neural models for Toronto area, Canada. Different neural models were used to carry out the forecasting works. One-day-ahead daily total load and peak load forecasts were implemented by using different neural models in order to find the more accurate forecasting results. The load data and temperatures provided by Independent Electricity System Operator (IESO) from January, year 2003 to January, year 2008 were studied and simulated. In our studies, mean absolute percentage error (MAPE) is used as the measurement of model´s performances.

Keywords

feedforward neural nets; load forecasting; power engineering computing; Canada; IESO; Independent Electricity System Operator; feedforward fully connected neural network; mean absolute percentage error; neural models; short term power load forecasting; Economic forecasting; Load forecasting; Neural networks; Power system modeling; Power system planning; Power system security; Power system simulation; Predictive models; Signal processing; Weather forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on

Conference_Location

Kaohsiung

Print_ISBN

978-1-4244-5543-0

Type

conf

DOI

10.1109/ICICIC.2009.330

Filename

5412329