Neural Networks for Wind Power Generation Forecasting: a Case Study

被引：0

作者：

Cancelliere, Rossella ^{[1
]}

Gosso, Alberto ^{[1
]}

Grosso, Andrea ^{[1
]}

机构：

[1] Univ Turin, Dept Comp Sci, I-10124 Turin, Italy

来源：

2013 10TH IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC) | 2013年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper uses data collected in a southern Italy wind farm to develop a neural network based prediction of the power produced by each turbine. First, some characteristics of wind turbine power generation are investigated. Then a careful data preprocessing is proposed to detect and remove outliers and to deal with damping, i.e. the effect of smoothing of wind speed caused by presence of other turbines. Besides, two different training algorithms for the most popular model, the multilayer perceptron, are analyzed, i.e. backpropagation and extreme learning machine (elm). The latter, when utilized together with a proposed data preprocessing technique, demonstrates to achieve better and more stable performance, despite its greater sensibility to overfitting.

引用

页码：666 / 671

页数：6

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