PV power forecasting using different artificial neural networks strategies

被引：0

作者：

Sansa, Ines ^{[1
]}

Missaoui, Sihem ^{[1
]}

Boussada, Zina ^{[1
]}

Bellaaj, Najiba Mrabet ^{[1
]}

Ahmed, Emad M. ^{[2
]}

Orabi, Mouhamed ^{[2
]}

机构：

[1] Univ Tunis El Manar, Ecole Natl Ingn Tunis, Lab Syst Elect LR11ES15, Tunis 1002, Tunisia

[2] Aswan Univ, Aswan Fac Engn, APEARC, Aswan City 81542, Egypt

来源：

2014 INTERNATIONAL CONFERENCE ON GREEN ENERGY | 2014年

关键词：

PV power forecasting; ANN; NARX model; model identification; ENERGY;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The integration of photovoltaic (PV), intermittent and uncontrollable power, into the electrical grid has become one of the major challenges for power system operators. Therefore the PV power forecasting can be beneficial in system planning and balancing energies. In this paper the PV power forecasting of a real generator [1] is presented. Different Artificial Neural Networks (ANN) strategies are used to forecast the PV power from meteorological variables, the radiation and the temperature. Simulation results corresponding to each ANN strategy are presented, discussed and compared. The dynamic ANN chosen in this work is the Nonlinear Auto Regressive models with eXogenous input (NARX model). Its performances have proved in the different time frame PV power forecasting. The impact of season's type on the efficiency of PV power forecasting is presented in the second part of this paper.

引用

页码：54 / 59

页数：6

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