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

被引:194
|
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
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
收藏
页码:362 / 371
页数:10
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