Modeling of photovoltaic array using random forests technique

被引：0

作者：

Ibrahim, Ibrahim A. ^{[1
]}

Mohamed, Azah ^{[1
]}

Khatib, Tamer ^{[2
]}

机构：

[1] Univ Kebangsaan Malaysia, Dept Elect Elect & Syst Engn, Bangi, Malaysia

[2] An Najah Natl Univ, Energy Engn & Environm Dept, Nablus, Israel

来源：

2015 IEEE CONFERENCE ON ENERGY CONVERSION (CENCON) | 2015年

关键词：

modeling of PV systems; random forests; performance evaluation; PERFORMANCE ANALYSIS; POWER;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a novel technique for modeling of photovoltaic (PV) array using random forests (RFs). Metrological variables such as solar radiation and ambient temperature as well as actual output current of a 3 kWp PV grid-connected system installed at Universiti Kebangsaan Malaysia have been utilized. These data are used to train and validate the proposed RFs model. Three statistical error values, namely, root mean square error (RMSE), mean bias error (MBE), and mean absolute percentage error (MAPE), are used to evaluate the developed model. The results show that the proposed RFs model accurately predicts the output current of the PV system. The RMSE, MAPE, and MBE values of the RFs model are 2.7482%, 8.7151%, and -2.5772%, respectively.

引用

页码：390 / 393

页数：4

共 17 条

[1] Photovoltaic solar system connected to the electric power grid operating as active power generator and reactive power compensator [J].

Albuquerque, Fabio L. ;

Moraes, Adelio J. ;

Guimaraes, Geraldo C. ;

Sanhueza, Sergio M. R. ;

Vaz, Alexandre R. .

SOLAR ENERGY, 2010, 84 (07) :1310-1317

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

Almonacid, F. ;

Perez-Higueras, P. J. ;

Fernandez, Eduardo F. ;

Hontoria, L. .

ENERGY CONVERSION AND MANAGEMENT, 2014, 85 :389-398

[3]

[Anonymous], 2014, RANDOM FORESTS 2004

[4] Illusions in regression analysis [J].

Armstrong, J. Scott .

INTERNATIONAL JOURNAL OF FORECASTING, 2012, 28 (03) :689-694

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

Bacher, Peder ;

Madsen, Henrik ;

Nielsen, Henrik Aalborg .

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

[6] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

[7] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

[8] IS CENTRAL STATION PHOTOVOLTAIC POWER DISPATCHABLE [J].

CHOWDHURY, BH ;

RAHMAN, S .

IEEE TRANSACTIONS ON ENERGY CONVERSION, 1988, 3 (04) :747-754

[9] Performance analysis of a multi crystalline Si photovoltaic module under Mugla climatic conditions in Turkey [J].

Eke, Rustu ;

Dernircan, Huseyin .

ENERGY CONVERSION AND MANAGEMENT, 2013, 65 :580-586

[10]

Ethem Alpaydin, 2011, INTRO MACHINE LEARNI

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