Current status of wind energy forecasting and a hybrid method for hourly predictions

被引：202

作者：

Okumus, Inci ^{[1
]}

Dinler, Ali ^{[2
]}

机构：

[1] Bulent Ecevit Univ, Art & Sci Fac, Dept Math, Zonguldak, Turkey

[2] Istanbul Medeniyet Univ, Dept Math, Istanbul, Turkey

来源：

ENERGY CONVERSION AND MANAGEMENT | 2016年 / 123卷

关键词：

Wind energy; Wind power; Wind energy forecasting; ARTIFICIAL NEURAL-NETWORKS; PARTICLE SWARM OPTIMIZATION; SPEED PREDICTION; POWER-GENERATION; TERM; MODEL; REGRESSION; TRANSFORM; ENSEMBLE; COLONY;

D O I：

10.1016/j.enconman.2016.06.053

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Generating accurate wind energy and/or power forecasts is crucially important for energy trading and planning. The present study initially gives an extensive review of recent advances in statistical wind forecasting. Numerous prediction methods for varying prediction horizons from a few seconds to several months are listed. Then in the light of accurate results in the literature, the present study combines the adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) for 1 h ahead wind speed forecasts. The performance results show the mean absolute percentage errors (MAPE) of 2.2598%, 3.3530% and 3.8589% at three different locations for daily average wind speeds. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：362 / 371

页数：10

共 87 条

[1] Extraction of the inherent nature of wind speed using wavelets and FFT [J].

Alam, Md. Mahbub ;

Rehman, S. ;

AI-Hadhrami, L. M. ;

Meyer, J. P. .

ENERGY FOR SUSTAINABLE DEVELOPMENT, 2014, 22 :34-47

[2] Wind speed and power forecasting based on spatial correlation models [J].

Alexiadis, MC ;

Dokopoulos, PS ;

Sahsamanoglou, HS .

IEEE TRANSACTIONS ON ENERGY CONVERSION, 1999, 14 (03) :836-842

[3] Wind farm power prediction based on wavelet decomposition and chaotic time series [J].

An, Xueli ;

Jiang, Dongxiang ;

Liu, Chao ;

Zhao, Minghao .

EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (09) :11280-11285

[4] RETRACTED: Artificial neural networks applications in wind energy systems: a review (Retracted article. See vol. 84, pg. 173, 2018) [J].

Ata, Rasit .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 49 :534-562

[5] Long-term wind speed and power forecasting using local recurrent neural network models [J].

Barbounis, TG ;

Theocharis, JB ;

Alexiadis, MC ;

Dokopoulos, PS .

IEEE TRANSACTIONS ON ENERGY CONVERSION, 2006, 21 (01) :273-284

[6] Very short-term wind power forecasting with neural networks and adaptive Bayesian learning [J].

Blonbou, Ruddy .

RENEWABLE ENERGY, 2011, 36 (03) :1118-1124

[7] Short term wind speed forecasting in La Venta, Oaxaca, Mexico, using artificial neural networks [J].

Cadenas, Erasmo ;

Rivera, Wilfrido .

RENEWABLE ENERGY, 2009, 34 (01) :274-278

[8] Forecasting wind speed with recurrent neural networks [J].

Cao, Qing ;

Ewing, Bradley T. ;

Thompson, Mark A. .

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2012, 221 (01) :148-154

[9]

Carpinone A, P 11 INT C PROB METH, P107

[10] Short-term wind power forecasting in Portugal by neural networks and wavelet transform [J].

Catalao, J. P. S. ;

Pousinho, H. M. I. ;

Mendes, V. M. F. .

RENEWABLE ENERGY, 2011, 36 (04) :1245-1251

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