Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

Author

Nose-Filho, Kenji ; Lotufo, A.D.P. ; Minussi, Carlos Roberto

Author_Institution

Dept. of Electr. Eng., Coll. of Eng. of Ilha Solteira (UNESP), Ilha Solteiraz, Brazil

fYear

2011

fDate

19-23 June 2011

Firstpage

1

Lastpage

7

Abstract

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data.

Keywords

load forecasting; neural nets; power engineering computing; power filters; regression analysis; substations; New Zealand electrical substation; general regression neural network; half-hourly load data preprocessing; moving average filter; noise handling; short-term multinodal load forecasting; Artificial neural networks; Load forecasting; Low pass filters; Neurons; Noise; Substations; Training; Artificial Neural Networks; Moving Average Filter; Short Term Load Forecasting; Signal Processing; Training Dataset;

fLanguage

English

Publisher

ieee

Conference_Titel

PowerTech, 2011 IEEE Trondheim

Conference_Location

Trondheim

Print_ISBN

978-1-4244-8419-5

Electronic_ISBN

978-1-4244-8417-1

Type

conf

DOI

10.1109/PTC.2011.6019428

Filename

6019428