Short-term forecasting of power production in a large-scale photovoltaic plant

被引：127

作者：

Mellit, A. ^{[1
,2
,4
]}

Pavan, A. Massi ^{[3
]}

Lughi, V. ^{[3
]}

机构：

[1] Jijel Univ, Fac Sci & Technol, Renewable Energy Lab, Jijel 18000, Algeria

[2] Unite Dev Equipements Solaires UDES EPST CDER, Bousmail 42000, Tipaza, Algeria

[3] Univ Trieste, Lab Nanotechnol & Photovolta, Dept Engn & Architecture, I-34127 Trieste, Italy

[4] Abdus Salam Int Ctr Theoret Phys ICTP, I-34151 Trieste, Italy

来源：

SOLAR ENERGY | 2014年 / 105卷

关键词：

Large-scale photovoltaic plant; Power; Forecasting; Artificial neural networks; ARTIFICIAL NEURAL-NETWORK; PREDICTION; MODULES; SYSTEMS;

D O I：

10.1016/j.solener.2014.03.018

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：

In this paper, a simple but accurate approach for short-term forecasting of the power produced by a Large-Scale Grid Connected Photovoltaic Plant (LS-GCPV) is presented. A 1-year database of solar irradiance, cell temperature and power output produced by a 1-MWp photovoltaic plant located in Southern Italy is used for developing three distinct artificial neural network (ANN) models, to be applied to three typical types of day (sunny, partly cloudy and overcast). The possibility of obtaining accurate results by using solely the monitored data rather than knowing the actual architecture and details of the plant is a notable advantage; in particular, the method's reliability gives to operation and maintenance and to grid operators excellent confidence in the evaluation of the performance of the plant and in the programming of the dispatching plans, respectively. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：401 / 413

页数：13

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