Day-Ahead Hourly Forecasting of Power Generation From Photovoltaic Plants

被引：173

作者：

Gigoni, Lorenzo ^{[1
]}

Betti, Alessandro ^{[1
]}

Crisostomi, Emanuele ^{[2
]}

Franco, Alessandro ^{[2
]}

Tucci, Mauro ^{[2
]}

Bizzarri, Fabrizio ^{[3
]}

Mucci, Debora ^{[4
]}

机构：

[1] IEM Srl, I-57121 Livorno, Italy

[2] Univ Pisa, Dept Energy Syst Terr & Construct Engn, I-56122 Pisa, Italy

[3] Enel Green Power SpA, I-00198 Rome, Italy

[4] Enel Green Power SpA, Renewable Energy Management, I-00198 Rome, Italy

来源：

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY | 2018年 / 9卷 / 02期

关键词：

PV plants; machine learning algorithms; power generation forecasts; ENSEMBLE; OUTPUT; TERM; PREDICTION; MODEL;

D O I：

10.1109/TSTE.2017.2762435

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The ability to accurately forecast power generation from renewable sources is nowadays recognized as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper, we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

引用

页码：831 / 842

页数：12

共 35 条

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