WIND POWER FORECASTING ACCURACY ASSESSMENT FOR MULTIPLE TIMESCALES

被引：0

作者：

Cristea, Cristian ^{[1
]}

Eremia, Mircea ^{[1
]}

Toma, Lucian ^{[1
]}

机构：

[1] Univ Politehn Bucuresti, Dept Elect Power Syst, Bucharest, Romania

来源：

UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE | 2015年 / 77卷 / 04期

关键词：

wind power generation forecast; artificial neural networks;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a wind power forecasting error accuracy assessment over multiple lead times, as original contribution, for a six-month study period from December 2013 to May 2014. The study was performed on a 2 MW wind turbine generator located near Galati-Romania. The forecast is performed using a specific tool, which is based on a multi-layer neural network method and which receives data from the wind turbine generator SCADA system. The accuracy of the sitespecific forecast is determined by comparing the observed/measured power production of the wind turbine generator system with the corresponding power forecast.

引用

页码：393 / 404

页数：12

共 50 条

[31] Optimized Forecasting Model to Improve the Accuracy of Very Short-Term Wind Power Prediction
Hossain, Md Alamgir
Gray, Evan
Lu, Junwei
Islam, Md Rabiul
Alam, Md Shafiul
Chakrabortty, Ripon
Pota, Hemanshu Roy
[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2023, 19 (10) : 10145 - 10159
[32] More than accuracy: end-to-end wind power forecasting that optimises the energy system
Wahdany, Dariush
Schmitt, Carlo
Cremer, Jochen L.
[J]. ELECTRIC POWER SYSTEMS RESEARCH, 2023, 221
[33] Based on Phase and Level Errors Translating and Interpolating Correction to Improve the Accuracy of Wind Power Forecasting
YaWei-hu
Jiang-Li
Zhe-Chen
YiBo-wang
Qin-Chao
[J]. 2015 IEEE ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC), 2015, : 552 - 558
[34] Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China
Zhao, Pan
Wang, Jiangfeng
Xia, Junrong
Dai, Yiping
Sheng, Yingxin
Yue, Jie
[J]. RENEWABLE ENERGY, 2012, 43 : 234 - 241
[35] Wind Power Forecasting Considering Wind Turbine Condition
Pei Yan
Qian Zheng
Chen Niya
[J]. 2015 IEEE INNOVATIVE SMART GRID TECHNOLOGIES - ASIA (ISGT ASIA), 2015,
[36] The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales
Wang, Qin
Wu, Hongyu
Florita, Anthony R.
Martinez-Anido, Carlo Brancucci
Hodge, Bri-Mathias
[J]. APPLIED ENERGY, 2016, 184 : 696 - 713
[37] AN OVERVIEW ON WIND POWER FORECASTING METHODS
Chai, Songjian
Xu, Zhao
Lai, Loi Lei
Wong, Kit Po
[J]. PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOL. 2, 2015, : 765 - 770
[38] A Review of Wind Power Forecasting & Prediction
Yang Mao
Wang Shaoshuai
[J]. 2016 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), 2016,
[39] Forecasting volatility of wind power production
Shen, Zhiwei
Ritter, Matthias
[J]. APPLIED ENERGY, 2016, 176 : 295 - 308
[40] Machine Learning for Wind Power Forecasting
Cardoso de Figueiredo, Yann Fabricio
Lima de Campos, Lidio Mauro
[J]. 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,

← 1 2 3 4 5 →