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

共 50 条

[41] Probabiistic Short-term Wind Power Forecasting Based on Deep Neural Networks [J].

Wu, Wenzu ;

Chen, Kunjin ;

Qiao, Ying ;

Lu, Zongxiang .

2016 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), 2016,

[42] Short-term wind power forecasting in Portugal by neural networks and wavelet transform [J].

Catalao, J. P. S. ;

Pousinho, H. M. I. ;

Mendes, V. M. F. .

RENEWABLE ENERGY, 2011, 36 (04) :1245-1251

[43] Multi Step Ahead Forecasting of Wind Power by Genetic Algorithm based Neural Networks [J].

Saroha, Sumit ;

Aggarwal, S. K. .

2014 6th IEEE Power India International Conference (PIICON), 2014,

[44] Wind Power Forecasting using Hybrid Recurrent Neural Networks with Empirical Mode Decomposition [J].

van Heerden, Liaan ;

Vermeulen, H. J. ;

van Staden, Chantelle .

2022 IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2022 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE), 2022,

[45] Forecasting enhancement of Wind Power Generation using Adversarial Networks : A Data Driven Approach [J].

Shelake, Swapnil ;

Kondavathini, Rishikesh ;

Ansari, Mahedin ;

Sonwadekar, Punit ;

Kumar, Sunny ;

Goswami, Prerna ;

Kazi, Faruk .

2022 IEEE PES 14TH ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE, APPEEC, 2022,

[46] Application of Wavelet and Neural Network Models for Wind Speed and Power Generation Forecasting in a Brazilian Experimental Wind Park [J].

de Aquino, Ronaldo R. B. ;

Lira, Milde M. S. ;

de Oliveira, Josinaldo B. ;

Carvalho, Manoel A., Jr. ;

Neto, Otoni N. ;

de Almeida, Givanildo J. .

IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, 2009, :1479-+

[47] Case Study of Weather Maintenance in Wind Power Generation [J].

Ángel M. Costa ;

Gholamreza Roshan ;

José A. Orosa ;

Ángel Rodríguez-Fernández .

Arabian Journal for Science and Engineering, 2014, 39 :5615-5624

[48] Case Study of Weather Maintenance in Wind Power Generation [J].

Costa, Angel M. ;

Roshan, Gholamreza ;

Orosa, Jose A. ;

Rodriguez-Fernandez, Angel .

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2014, 39 (07) :5615-5624

[49] Conditioning Neural Networks: A Case Study of Electricity Load Forecasting [J].

Hosseini, Hossein ;

Hooshmand, Ali ;

Sharma, Ratnesh .

2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, :2190-2196

[50] Modeling of Brazilian Wind Power Generation Capacity: A Multivariate Analysis with Neural Networks [J].

Dos Santos, Daiane Rodrigues ;

Barcellos, Tuany Esthefany ;

Costa, Tiago ;

Marques, Maria Laura ;

Prado, Daniela ;

Castro, Reinaldo .

Renewable Energy and Power Quality Journal, 2024, 22 (03) :1-8

← 1 2 3 4 5 →