Multi-step ahead wind power forecasting for Ireland using an ensemble of VMD-ELM models

被引:0
|
作者
Gonzalez-Sopena, Juan Manuel [1 ]
Pakrashi, Vikram [2 ,3 ]
Ghosh, Bidisha [4 ]
机构
[1] Trinity Coll Dublin, Dept Civil Struct & Environm Engn, Dublin, Ireland
[2] Univ Coll Dublin, SFI MaREI Ctr, Sch Mech & Mat Engn, Dynam Syst & Risk Lab, Dublin, Ireland
[3] Univ Coll Dublin, Energy Inst, Dublin, Ireland
[4] Trinity Coll Dublin, Sch Engn, Quant Grp, Connect SFI Res Ctr Future Networks & Commun, Dublin, Ireland
关键词
wind power forecasting; variational mode decomposition; extreme learning machine; multi-step forecasting; DECOMPOSITION; PREDICTION; SPECTRUM;
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
Accurate wind power forecasts are a key tool for the correct operation of the grid and the energy trading market, particularly in regions with a large wind resource as Ireland, where wind energy comprises a large share of the electricity generated. A multi-step ahead wind power forecasting ensemble of models based on variational mode decomposition and extreme learning machines is employed in this paper to be applied for Irish wind farms. Data from two wind farms placed in different locations are used to show the suitability of the model for Ireland. The results show that the use of this full ensemble of models provides more reliable and robust forecasts for several prediction horizons and an improvement between 7% and 22% with respect to a single model. Additionally, the ensemble shows a low systematic error regardless of the prediction horizon.
引用
收藏
页码:187 / 191
页数:5
相关论文
共 50 条
  • [41] Multi-Step Forecasting for Household Power Consumption
    Zheng, Yuanzhang
    Xu, Zhen
    Liao, Wei
    Lin, Binbin
    Chen, Jing
    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2023, 18 (08) : 1255 - 1263
  • [42] Multi-Step wind power forecasting model Using LSTM networks, Similar Time Series and LightGBM
    Cao, Yukun
    Gui, Liai
    2018 5TH INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI), 2018, : 192 - 197
  • [43] Multi-step ahead wind power forecasting based on multi-feature wavelet decomposition and convolution-gated recurrent unit model
    Shringi, Shubham
    Saini, Lalit Mohan
    Aggarwal, Sanjeev Kumar
    ELECTRICAL ENGINEERING, 2025,
  • [44] Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-step-ahead Solar Power Forecasting
    Koprinska, Irena
    Rana, Mashud
    Rahman, Ashfaqur
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: TEXT AND TIME SERIES, PT IV, 2019, 11730 : 436 - 449
  • [45] Multi-step ahead time-series wind speed forecasting for smart-grid application
    Malik, Hasmat
    Khurshaid, Tahir
    Almutairi, Abdulaziz
    Alotaibi, Majed A.
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2022, 42 (02) : 633 - 646
  • [46] Hybrid Approach Combining SARIMA and Neural Networks for Multi-Step Ahead Wind Speed Forecasting in Brazil
    Alencar, David B.
    Affonso, Carolina M.
    Oliveira, Roberto C. L.
    Filho, Jose C. R.
    IEEE ACCESS, 2018, 6 : 55986 - 55994
  • [47] Ensemble Learning Models for Wind Power Forecasting
    Deon, Samara
    de Lima, Jose Donizetti
    Dranka, Geremi Gilson
    Dal Molin Ribeiro, Matheus Henrique
    Santos dos Anjos, Julio Cesar
    de Paz Santana, Juan Francisco
    Quietinho Leithardt, Valderi Reis
    NEW TRENDS IN DISRUPTIVE TECHNOLOGIES, TECH ETHICS, AND ARTIFICIAL INTELLIGENCE, DITTET 2024, 2024, 1459 : 15 - 27
  • [48] An ensemble multi-step M-RMLSSVR model based on VMD and two-group strategy for day-ahead short-term load forecasting
    Yuan, Fang
    Che, Jinxing
    KNOWLEDGE-BASED SYSTEMS, 2022, 252
  • [49] A Multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting
    Ruano, Maria da Graca
    Ruano, Antonio
    ENERGIES, 2024, 17 (03)
  • [50] An Intelligent Method for Very-Short Range Multi-Step Wind Power Forecasting
    Ahmed, Adil
    Khalid, Muhammad
    2018 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2018,