Short-term power forecasting system for photovoltaic plants

被引：159

作者：

Alfredo Fernandez-Jimenez, L. ^{[1
]}

Munoz-Jimenez, Andres ^{[1
]}

Falces, Alberto ^{[1
]}

Mendoza-Villena, Montserrat ^{[1
]}

Garcia-Garrido, Eduardo ^{[1
]}

Lara-Santillan, Pedro M. ^{[1
]}

Zorzano-Alba, Enrique ^{[1
]}

Zorzano-Santamaria, Pedro J. ^{[1
]}

机构：

[1] Univ La Rioja, Dept Elect Engn, ETS Ingn Ind, Logrono 26004, Spain

来源：

RENEWABLE ENERGY | 2012年 / 44卷

关键词：

Short-term forecasting; Photovoltaic systems; Prediction; ARTIFICIAL-INTELLIGENCE TECHNIQUES; HOURLY SOLAR-RADIATION; NEURAL-NETWORKS; PREDICTION; IRRADIANCE; ANFIS; MODEL;

D O I：

10.1016/j.renene.2012.01.108

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This paper presents a new statistical short-term forecasting system for a grid-connected photovoltaic (PV) plant. The proposed system comprises three modules composed of two numerical weather prediction models and an artificial neural network based model. The first two modules are used to forecast weather variables used by the third module, which has been selected from a set of different models. The final forecast value is the hourly energy production in the PV plant. The forecasting horizon ranges from 1 to 39 h, covering all of the following day. The forecast values can be used for determining the most favourable hours to carry out maintenance tasks in the plant, and for preparing bid offers to the electricity market. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：311 / 317

页数：7

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