A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

被引：118
作者：
Pousinho, H. M. I. [1 ]
Mendes, V. M. F. [2 ]
Catalao, J. P. S. [1 ,3 ]
机构：
[1] Univ Beira Interior, Dept Electromech Engn, P-6201001 Covilha, Portugal
[2] Inst Super Engn Lisboa, Dept Elect Engn & Automat, P-1950062 Lisbon, Portugal
[3] Univ Tecn Lisboa, Inst Super Tecn, Ctr Innovat Elect & Energy Engn, P-1049001 Lisbon, Portugal
来源：
ENERGY CONVERSION AND MANAGEMENT | 2011年 / 52卷 / 01期
关键词：
Wind power; Prediction; Swarm optimization; Neuro-fuzzy; NEURAL-NETWORK APPROACH; SPEED; SYSTEM;
D O I：
10.1016/j.enconman.2010.07.015
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码：397 / 402
页数：6
相关论文
共 27 条
  • [1] Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks
    Bashir, Z. A.
    El-Hawary, M. E.
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2009, 24 (01) : 20 - 27
  • [2] Catalao JPS, 2007, ENG INTELL SYST ELEC, V15, P15
  • [3] Catalao JPS, 2009, ENG INTELL SYST ELEC, V17, P5, DOI 10.1109/ISAP.2009.5352853
  • [4] Day-ahead electricity price forecasting using the wavelet transform and ARIMA models
    Conejo, AJ
    Plazas, MA
    Espínola, R
    Molina, AB
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2005, 20 (02) : 1035 - 1042
  • [5] A review on the young history of the wind power short-term prediction
    Costa, Alexandre
    Crespo, Antonio
    Navarro, Jorge
    Lizcano, Gil
    Madsen, Henrik
    Feitosa, Everaldo
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2008, 12 (06) : 1725 - 1744
  • [6] The actual effect of wind power on overall electricity generation costs and CO2 emissions
    Delarue, Erik D.
    Luickx, Patrick J.
    D'haeseleer, William D.
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2009, 50 (06) : 1450 - 1456
  • [7] Predicting the wind
    Ernst, Bernhard
    Oakleaf, Brett
    Ahlstrom, Mark L.
    Lange, Matthias
    Moehrlen, Corinna
    Lange, Bernhard
    Focken, Ulrich
    Rohrig, Kurt
    [J]. IEEE POWER & ENERGY MAGAZINE, 2007, 5 (06): : 78 - 89
  • [8] Forecasting the Wind Generation Using a Two-Stage Network Based on Meteorological Information
    Fan, Shu
    Liao, James R.
    Yokoyama, Ryuichi
    Chen, Luonan
    Lee, Wei-Jen
    [J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 2009, 24 (02) : 474 - 482
  • [9] Mycielski approach for wind speed prediction
    Hocaoglu, Fatih O.
    Fidan, Mehmet
    Gerek, Oemer N.
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2009, 50 (06) : 1436 - 1443
  • [10] ANFIS - ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM
    JANG, JSR
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1993, 23 (03): : 665 - 685
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