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

被引：117
|
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 50 条
  • [41] A Hybrid Approach for Short-term Wind Speed Prediction in Huan County of China
    Fu, Tonglin
    Yang, Mingxia
    Ju, Pengyue
    Liu, Kun
    Zhao, Huani
    2019 5TH INTERNATIONAL CONFERENCE ON ENERGY MATERIALS AND ENVIRONMENT ENGINEERING, 2019, 295
  • [42] Short-term Prediction Models for Wind Speed and Wind Power
    Bai, Guangxing
    Ding, Yanwu
    Yildirim, Mehmet Bayram
    Ding, Yan-Hong
    2014 2ND INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI), 2014, : 180 - 185
  • [43] A hybrid technique for short-term wind speed prediction
    Hu, Jianming
    Wang, Jianzhou
    Ma, Kailiang
    ENERGY, 2015, 81 : 563 - 574
  • [44] A hybrid strategy of short term wind power prediction
    Peng, Huaiwu
    Liu, Fangrui
    Yang, Xiaofeng
    RENEWABLE ENERGY, 2013, 50 : 590 - 595
  • [45] Short-term wind power prediction and error analysis
    Ma, Rui
    Wang, Lingling
    Hu, Shuju
    RENEWABLE ENERGY AND ENVIRONMENTAL TECHNOLOGY, PTS 1-6, 2014, 448-453 : 1851 - 1857
  • [46] Short-Term Prediction of Wind Power Based on Deep Long Short-Term Memory
    Qu Xiaoyun
    Kang Xiaoning
    Zhang Chao
    Jiang Shuai
    Ma Xiuda
    2016 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2016, : 1148 - 1152
  • [47] Short-term wind power prediction based on combined long short-term memory
    Zhao, Yuyang
    Li, Lincong
    Guo, Yingjun
    Shi, Boming
    Sun, Hexu
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2024, 18 (05) : 931 - 940
  • [48] A review on short-term and ultra-short-term wind power prediction
    Xue, Yusheng
    Yu, Chen
    Zhao, Junhua
    Li, Kang
    Liu, Xueqin
    Wu, Qiuwei
    Yang, Guangya
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2015, 39 (06): : 141 - 151
  • [49] A Hybrid Approach for Short-Term Forecasting of Wind Speed
    Tatinati, Sivanagaraja
    Veluvolu, Kalyana C.
    SCIENTIFIC WORLD JOURNAL, 2013,
  • [50] A hybrid PSO-ANFIS model for predicting unstable zones in underground roadways
    Mahdevari, Satar
    Khodabakhshi, Mohammad Bagher
    TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, 2021, 117
12345