Wind Power Forecasting with Machine Learning: Single and combined methods

被引：0

作者：

Rosa J. ^{[1
]}

Pestana R. ^{[1
,2
,3
]}

Leandro C. ^{[1
]}

Geraldes C. ^{[1
,4
]}

Esteves J. ^{[3
]}

Carvalho D. ^{[1
]}

机构：

[1] Department of Mathematics, Electrical Engineering, Instituto Superior de Engenharia de Lisboa, Lisbon

[2] System Operator Division REN-Rede Eléctrica Nacional, S.A., Lisbon

[3] R&D NESTER Centro de Investigação em Energia REN-State Grid, S.A., Lisbon

[4] CEAUL, Centro de Estatística e Aplicações, Universidade de Lisboa, Lisbon

来源：

Renewable Energy and Power Quality Journal | 2022年 / 20卷

关键词：

ensemble models; feature engineering; machine learning; recurrent neural network; Wind power forecast;

D O I：

10.24084/repqj20.397

中图分类号：

学科分类号：

摘要：

In Portugal, wind power represents one of the largest renewable sources of energy in the national energy mix. The investment in wind power started several decades ago and is still on the roadmap of political and industrial players. One example is that by 2030 it is estimated that wind power is going to represent up to 35% of renewable energy production in Portugal. With the growth of the installed wind capacity, the development of methods to forecast the amount of energy generated becomes increasingly necessary. Historically, Numerical Weather Prediction (NWP) models were used. However, forecasting accuracy depends on many variables such as on-site conditions, surrounding terrain relief, local meteorology, etc. Thus, it becomes a challenge to obtain improved results using such methods. This article aims to report the development of a machine learning pipeline with the objective of improving the forecasting capability of the NWP’s to obtain an error lower than 10%. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

引用

页码：673 / 678

页数：5

共 16 条

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