Non-stationary Signal Forecasting by Neural Network with Modified Neurons

Author

Huang, Chih-Chien ; Lin, Yi-Ching ; Chen, Yu-Ju ; Wang, Shuming T. ; Hwang, Rey-Chue

Author_Institution

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

Volume

2

fYear

2010

fDate

13-14 March 2010

Firstpage

785

Lastpage

788

Abstract

This paper presents the non-stationary power signal forecasting by using a neural network with modified neurons for PJM data set provided by Independent Electricity System Operator (IESO). In this data set, the load information is the sum of power load consumed by three areas, including Allentown, Baltimore and Philadelphia. The historical load and temperature information from year 2003 to year 2008 were studied and simulated. The forecasts of one-day-ahead daily total load and peak load were implemented. In order to find the accurate forecasting results, different combinations of inputs were carried out. In this study, mean absolute percentage error (MAPE) is used as the measurement of forecasting performances.

Keywords

load forecasting; neural nets; power engineering computing; PJM data set; independent electricity system operator; mean absolute percentage error; neural network; nonstationary power signal load forecasting; Companies; Load forecasting; Neural networks; Neurons; Power measurement; Power system modeling; Power system planning; Predictive models; Signal processing; Technology forecasting; forecasting; load; modified neurons; neural model;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on

Conference_Location

Changsha City

Print_ISBN

978-1-4244-5001-5

Electronic_ISBN

978-1-4244-5739-7

Type

conf

DOI

10.1109/ICMTMA.2010.173

Filename

5460263