A Hierarchical Approach to Probabilistic Wind Power Forecasting

被引：0
作者：
Gilbert, Ciaran [1 ]
Browell, Jethro [1 ]
McMillan, David [1 ]
机构：
[1] Univ Strathclyde, Glasgow, Lanark, Scotland
来源：
2018 IEEE INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS) | 2018年
基金：
英国工程与自然科学研究理事会;
关键词：
Wind power; probabilistic forecasting; hierarchical forecasting; forecasting; wind power integration; REGULARIZATION;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
This paper describes a method to generate improved probabilistic wind farm power forecasts in a hierarchical framework with the incorporation of production data from individual wind turbines. Wind power forms a natural hierarchy as generated electricity is aggregated from the individual turbine, to farm, to the regional level and so on. To forecast the wind farm power generation, a layered approach is proposed whereby deterministic forecasts from the lower layer (turbine level) are used as input features to an upper-level (wind farm) probabilistic model. In a case study at a utility scale wind farm it is shown that improvements in probabilistic forecast skill (CRPS) of 1.24% and 2.39% are obtainable when compared to two very competitive benchmarks based on direct forecasting of the wind farm power using Gradient Boosting Trees and an Analog Ensemble, respectively.
引用
收藏
页数：6
相关论文
共 50 条
[21]   Hybrid Probabilistic Wind Power Forecasting Using Temporally Local Gaussian Process [J].
Yan, Juan ;
Li, Kang ;
Bai, Er-Wei ;
Deng, Jing ;
Foley, Aoife M. .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2016, 7 (01) :87-95
[22]   Time-adaptive quantile-copula for wind power probabilistic forecasting [J].
Bessa, Ricardo J. ;
Miranda, V. ;
Botterud, A. ;
Zhou, Z. ;
Wang, J. .
RENEWABLE ENERGY, 2012, 40 (01) :29-39
[23]   Operating Reserve Quantification Using Prediction Intervals of Wind Power: An Integrated Probabilistic Forecasting and Decision Methodology [J].
Zhao, Changfei ;
Wan, Can ;
Song, Yonghua .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2021, 36 (04) :3701-3714
[24]   Nonparametric Stochastic Differential Equations for Ultra-Short-Term Probabilistic Forecasting of Wind Power Generation [J].
Xu, Yuqi ;
Wan, Can ;
Yang, Guangya ;
Ju, Ping .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2025, 40 (03) :2179-2191
[25]   Probabilistic wind power forecasting with online model selection and warped gaussian process [J].
Kou, Peng ;
Liang, Deliang ;
Gao, Feng ;
Gao, Lin .
ENERGY CONVERSION AND MANAGEMENT, 2014, 84 :649-663
[26]   Wind Power Forecasting Error Distributions and Probabilistic Load Dispatch [J].
Lujano-Rojas, J. M. ;
Osorio, G. J. ;
Matias, J. C. O. ;
Catalao, J. P. S. .
2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM), 2016,
[27]   A new hybrid model for point and probabilistic forecasting of wind power [J].
Tahmasebifar, Reza ;
Moghaddam, Mohsen Parsa ;
Sheikh-El-Eslami, Mohammad Kazem ;
Kheirollahi, Reza .
ENERGY, 2020, 211
[28]   Advanced Deep Learning Approach for Probabilistic Wind Speed Forecasting [J].
Afrasiabi, Mousa ;
Mohammadi, Mohammad ;
Rastegar, Mohammad ;
Afrasiabi, Shahabodin .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (01) :720-727
[29]   Transferable wind power probabilistic forecasting based on multi-domain adversarial networks [J].
Dong, Xiaochong ;
Sun, Yingyun ;
Dong, Lei ;
Li, Jian ;
Li, Yan ;
Di, Lei .
ENERGY, 2023, 285
[30]   Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting [J].
Hong, Tao ;
Xie, Jingrui ;
Black, Jonathan .
INTERNATIONAL JOURNAL OF FORECASTING, 2019, 35 (04) :1389-1399
12345