Weather-based Machine Learning Technique for Day-Ahead Wind Power Forecasting

被引：0

作者：

Dobra, A. ^{[1
]}

Gandelli, A. ^{[1
]}

Grimaccia, F. ^{[1
]}

Leva, S. ^{[1
]}

Mussetta, M. ^{[1
]}

机构：

[1] Politecn Milan, Dept Energy, Via La Masa 34, I-20156 Milan, Italy

来源：

2017 IEEE 6TH INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY RESEARCH AND APPLICATIONS (ICRERA) | 2017年

关键词：

Wind Power Forecasting; Wind Energy; Wind Farm; Artificial Neural Network;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This paper presents the development of forecast models for a wind farm producibility with a 24 hours horizon. The aim is to obtain accurate wind power predictions by using feedforward artificial neural networks. In particular, different forecasting models arc developed and for each of them the best architecture is researched by means of sensitivity analysis, modifying the main parameters of the artificial neural network. The results obtained are compared with the forecasts provided by numerical weather prediction models (NWP).

引用

页码：206 / 209

页数：4

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