A Hybrid Method for Short-Term Wind Speed Forecasting

被引：27
|
作者：
Zhang, Jinliang [1 ,2 ]
Wei, YiMing [2 ,3 ]
Tan, Zhong-fu [1 ]
Wang, Ke [2 ,3 ]
Tian, Wei [4 ]
机构：
[1] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
[2] Beijing Inst Technol, Ctr Energy & Environm Policy Res, Beijing 100181, Peoples R China
[3] Beijing Inst Technol, Sch Management & Econ, Beijing 102206, Peoples R China
[4] IIT, Sch Management & Econ, Chicago, IL 60616 USA
来源：
SUSTAINABILITY | 2017年 / 9卷 / 04期
基金：
美国国家科学基金会;
关键词：
short-term wind speed forecasting; ensemble empirical mode decomposition (EEMD); adaptive neural network based fuzzy inference system (ANFIS); seasonal auto-regression integrated moving average (SARIMA); ARTIFICIAL NEURAL-NETWORKS; SPATIAL CORRELATION; POWER-GENERATION; PREDICTION; MODELS; SYSTEM; ENERGY;
D O I：
10.3390/su9040596
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method.
引用
收藏
页数：10
相关论文
共 50 条
  • [31] Short-term wind speed forecasting based on EMD and ANN
    Wang, Shao
    Yang, Jiang-Ping
    Li, Feng-Bing
    Liu, Ting-Lei
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2012, 40 (10): : 6 - 11
  • [32] Multistep short-term wind speed forecasting using transformer
    Wu, Huijuan
    Meng, Keqilao
    Fan, Daoerji
    Zhang, Zhanqiang
    Liu, Qing
    ENERGY, 2022, 261
  • [33] Short-term Wind Speed Forecasting Based on GCN and FEDformer
    Sun, Yihao
    Liu, Hao
    Hu, Tianyu
    Wang, Fei
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2024, 44 (21): : 8496 - 8506
  • [34] Short-Term Wind Speed Forecasting Using Ensemble Learning
    Karthikeyan, M.
    Rengaraj, R.
    2021 7TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENERGY SYSTEMS (ICEES), 2021, : 502 - 506
  • [35] A hybrid technique for short-term wind speed prediction
    Hu, Jianming
    Wang, Jianzhou
    Ma, Kailiang
    ENERGY, 2015, 81 : 563 - 574
  • [36] A Novel Method for Short-Term Wind Speed Forecasting Based on UPQPSO-LSSVM
    Nie, Wangxue
    Fu, Jingqi
    Sun, Sizhou
    ADVANCED COMPUTATIONAL METHODS IN ENERGY, POWER, ELECTRIC VEHICLES, AND THEIR INTEGRATION, LSMS 2017, PT 3, 2017, 763 : 32 - 42
  • [37] A Short-term Wind Power Forecasting Method Based on NWP Wind Speed Fluctuation Division and Clustering
    Li, Quanhui
    Lv, Ji
    Ding, Min
    Li, Danyun
    Fang, Zhijian
    2023 IEEE 6TH INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER-PHYSICAL SYSTEMS, ICPS, 2023,
  • [38] Short-term wind speed forecasting based on a hybrid model of ICEEMDAN, MFE, LSTM and informer
    Wang Xinxin
    Shen Xiaopan
    Ai Xueyi
    Li Shijia
    PLOS ONE, 2023, 18 (09):
  • [39] A hybrid intelligent framework for forecasting short-term hourly wind speed based on machine learning
    Wang, Yelin
    Yang, Ping
    Zhao, Shunyu
    Chevallier, Julien
    Xiao, Qingtai
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 213
  • [40] Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model
    Yousuf, Muhammad Uzair
    Al-Bahadly, Ibrahim
    Avci, Ebubekir
    IEEE ACCESS, 2021, 9 (09): : 79695 - 79711
12345