Applying wavelets to predict solar PV output power using generalized regression neural network

Author

Mandal, P. ; Haque, Ashraf U. ; Madhira, Surya T. S. ; Al-Hakeem, Donna I.

Author_Institution

Dept. of Ind., Manuf. & Syst. Eng., Univ. of Texas at El Paso, El Paso, TX, USA

fYear

2013

fDate

22-24 Sept. 2013

Firstpage

1

Lastpage

5

Abstract

This paper presents a hybrid intelligent approach to forecast short-term output power of a PV system. The proposed hybrid method is composed of a data filtering technique based on wavelet transform (WT) and generalized regression neural network (GRNN). In order to validate the prediction capability of the proposed WT+GRNN model, test results are compared with other soft computing models (SCMs). This paper uses a PV system data derived from Ashland, Oregon. Simulation results demonstrate the greater ability of GRNN model to handle nonlinear solar PV time-series data, and when it is combined with the WT, the forecasting accuracy is greatly enhanced.

Keywords

load forecasting; neural nets; photovoltaic power systems; power engineering computing; regression analysis; wavelet transforms; Ashland; GRNN; Oregon; SCM; WT; data filtering technique; generalized regression neural network; hybrid intelligent approach; short-term output power forecast; soft computing models; solar PV output power prediction; wavelet transform; Accuracy; Filtering; Forecasting; Hybrid power systems; Neural networks; Power generation; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

North American Power Symposium (NAPS), 2013

Conference_Location

Manhattan, KS

Type

conf

DOI

10.1109/NAPS.2013.6666912

Filename

6666912