A Hybrid Neuro-Evolutionary Algorithm for Wind Power Ramp Events Detection

被引：3

作者：

Cornejo-Bueno, Laura ^{[1
]}

Aybar-Ruiz, Adrian ^{[1
]}

Camacho-Gomez, Carlos ^{[1
]}

Prieto, Luis ^{[1
]}

Barea-Ropero, Alberto ^{[1
]}

Salcedo-Sanz, Sancho ^{[1
]}

机构：

[1] Univ Alcala, Dept Signal Proc & Commun, Madrid, Spain

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT I | 2017年 / 10305卷

关键词：

Evolutionary algorithms; Extreme Learning Machine; SMOTE; Wind power ramp events; EXTREME LEARNING-MACHINE; FEATURE-SELECTION; PREDICTION; SPEED;

D O I：

10.1007/978-3-319-59153-7_64

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work, a hybrid system for wind power ramps events prediction in wind farms is proposed. The system is based on modelling the prediction problem as a binary classification problem from atmospheric reanalysis data inputs. On the other hand, a hybrid neuro-evolutive algorithm is proposed, which combines Artificial Neuronal Networks such as Extreme Learning Machines, with evolutionary algorithms to optimize the trained models. The phenomenon under study occurs with a very low probability, for this reason the problem is so unbalanced, and it is necessary to resort to techniques focused on obtain good results by means of a reduction of the samples from the majority class, as the SMOTE approach. A feature selection is performed by the evolutionary algorithm in order to choose the best trained model. Finally, this model is evaluated by a test set and its accuracy performance is given. The accuracy obtained in the results is quite good in terms of classification performance.

引用

页码：745 / 756

页数：12

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