A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

被引：168

作者：

Almonacid, F. ^{[1
]}

Perez-Higueras, P. J. ^{[1
]}

Fernandez, Eduardo F. ^{[1
]}

Hontoria, L. ^{[1
]}

机构：

[1] Univ Jaen, Ctr Adv Studies Energy & Environm, Jaen, Spain

来源：

ENERGY CONVERSION AND MANAGEMENT | 2014年 / 85卷

关键词：

Artificial neural network; Short-term forecasting; PV generator; GLOBAL SOLAR-RADIATION; TIME-SERIES; AMBIENT-TEMPERATURE; PREDICTION; ENERGY; IRRADIANCE; MODELS; PLANT;

D O I：

10.1016/j.enconman.2014.05.090

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

One of the problems of some renewables energies is that the output of these kinds of systems is non-dispatchable depending on variability of weather conditions that cannot be predicted and controlled. From this point of view, the short-term forecast is going to be essential for effectively integrating solar energy sources, being a very useful tool for the reliability and stability of the grid ensuring that an adequate supply is present. In this paper a new methodology for forecasting the output of a PV generator one hour ahead based on dynamic artificial neural network is presented. The results of this study show that the proposed methodology could be used to forecast the power output of PV systems one hour ahead with an acceptable degree of accuracy. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：389 / 398

页数：10

共 48 条

[1] Short-term power forecasting system for photovoltaic plants [J].

Alfredo Fernandez-Jimenez, L. ;

Munoz-Jimenez, Andres ;

Falces, Alberto ;

Mendoza-Villena, Montserrat ;

Garcia-Garrido, Eduardo ;

Lara-Santillan, Pedro M. ;

Zorzano-Alba, Enrique ;

Zorzano-Santamaria, Pedro J. .

RENEWABLE ENERGY, 2012, 44 :311-317

[2] Generation of ambient temperature hourly time series for some Spanish locations by artificial neural networks [J].

Almonacid, F. ;

Perez-Higueras, P. ;

Rodrigo, P. ;

Hontoria, L. .

RENEWABLE ENERGY, 2013, 51 :285-291

[3] Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks [J].

Almonacid, F. ;

Rus, C. ;

Perez-Higueras, P. ;

Hontoria, L. .

ENERGY, 2011, 36 (01) :375-384

[4] Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods [J].

Almonacid, F. ;

Rus, C. ;

Hontoria, L. ;

Munoz, F. J. .

RENEWABLE ENERGY, 2010, 35 (05) :973-980

[5] Estimation of the energy of a PV generator using artificial neural network [J].

Almonacid, F. ;

Rus, C. ;

Perez, P. J. ;

Hontoria, L. .

RENEWABLE ENERGY, 2009, 34 (12) :2743-2750

[6] Characterisation of Si-crystalline PV modules by artificial neural networks [J].

Almonacid, F. ;

Rus, C. ;

Hontoria, L. ;

Fuentes, M. ;

Nofuentes, G. .

RENEWABLE ENERGY, 2009, 34 (04) :941-949

[7]

Alonso-Abella M, 2004, 19 EUR PHOT SOL EN C

[8]

AMATO U, 1989, J APPL METEOROL, V28, P711, DOI 10.1175/1520-0450(1989)028<0711:SPAPMO>2.0.CO

[9]

[10] Online short-term solar power forecasting [J].

Bacher, Peder ;

Madsen, Henrik ;

Nielsen, Henrik Aalborg .

SOLAR ENERGY, 2009, 83 (10) :1772-1783

← 1 2 3 4 5 →