1 |
IRENA. Wind energy [EB/OL]. 2021. .
URL
|
2 |
LIU H, CHEN C. Data processing strategies in wind energy forecasting models and applications: a comprehensive review[J]. Applied Energy, 2019, 249(SEP.):392-408.
|
3 |
JUNG J, BROADWATER R P. Current status and future advances for wind speed and power forecasting[J]. Renewable & Sustainable Energy Reviews, 2014, 31(MAR.):762-777.
|
4 |
MA Leiming. Development of artificial intelligence technology in weather forecast[J]. Advances in Earth Science, 2020, 35(6):551-560.
|
|
马雷鸣. 天气预报中的人工智能技术进展[J]. 地球科学进展,2020,35(6): 551-560.
|
5 |
CAO B, ZHAO J W, Lü Z H, et al. Multi-objective evolution of fuzzy rough neural network via distributed parallelism for stock prediction[J]. IEEE Transactions on Fuzzy Systems, 2020, 28(5): 939-952.
|
6 |
WANG Bingdi, LI Qingquan, SHEN Xinyong, et al. Climatological characteristics of the East Asian winter monsoon simulated by CWRF regional climate model[J]. Advances in Earth Science, 2020, 35(3):319-330.
|
|
王冰笛,李清泉,沈新勇,等. 区域气候模式 CWRF 对东亚冬季风气候特征的模拟[J]. 地球科学进展,2020,35(3):319-330.
|
7 |
HOOLOHAN V, TOMLIN A S, COCKERILL T. Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data[J]. Renewable Energy, 2018, 126 (OCT.) :1 043-1 054.
|
8 |
ALLEN D J, TOMLIN A S, BALE C S E, et al. A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data[J]. Applied Energy, 2017, 208(15):1 246-1 257.
|
9 |
Guoqin Lü, ZHANG Huiqin, LI Jiachun. Characteristics and forecast of short-term wind speed series in the Donghai Bridge wind farm [J]. Scientia Sinica (Physica, Mechanica & Astronomica), 2016, 46(12):124713.
|
|
吕国钦,张会琴,李家春. 东海大桥风电场短期风速序列特性及其预报[J]. 中国科学:物理学力学天文学,2016,46(12):124713.
|
10 |
LIU H, TIAN H Q, LI Y F. Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction[J]. Applied Energy, 2012, 98(OCT.): 415-424.
|
11 |
WANG L, LI X, BAI Y. Short-term wind speed prediction using an extreme learning machine model with error correction[J]. Energy Conversion and Management, 2018, 162(APR.):239-250.
|
12 |
SOMAN S, ZAREIPOUR H, MALIK O, et al. A review of wind power and wind speed forecasting methods with different time horizons[C]. North American Power Symposium (NAPS), USA: IEEE, 2010.
|
13 |
NOOROLLAHI Y, JOKAR M A, KALHOR A. Using artificial neural networks for temporal and spatial wind speed forecasting in Iran[J]. Energy Conversion and Management, 2016, 115(May.): 17-25.
|
14 |
KONAR A, BHATTACHARYA D. Time-series prediction and application: a machine intelligence approach[M]. Intelligent System Reference Library,2017
|
15 |
PARK C, LEE C, HONG L, et al. S2-Net: machine reading comprehension with SRU-based self-matching networks[J]. ETRI Journal, 2019, 41(3): 371-382.
|
16 |
BENMOUIZA K, CHEKNANE A. Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models[J]. Theoretical & Applied Climatology, 2016, 124(3/4): 945-958.
|
17 |
YU C, LI Y, XIANG H, et al. Data mining-assisted short-term wind speed forecasting by wavelet packet decomposition and elman neural network[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2018, 175(APR.): 136-143.
|
18 |
LIU M D, DING L, BAI Y L. Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction[J]. Energy Conversion and Management, 2021, 233(FEB.): 113917.
|
19 |
TANG G, WU Y, LI C, et al. A novel wind speed interval prediction based on error prediction method[J]. IEEE Transactions on Industrial Informatics, 2020, 16(11): 6 806-6 815.
|
20 |
WANG Jun, LI Xia, ZHOU Xidong, et al. Ultra-short-term wind speed prediction based on VMD-LSTM[J]. Power System Protection and Control, 2020, 48(11): 45-52.
|
|
王俊,李霞,周昔东,等. 基于VMD和LSTM的超短期风速预测[J]. 电力系统保护与控制,2020,48(11):45-52.
|
21 |
LIU H, MI X, LI Y. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM [J]. Energy Conversion and Management, 2018, 159(MAR.): 54-64.
|
22 |
FEI S W, HE Y. Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine [J]. International Journal of Electrical Power & Energy Systems, 2015, 73 (DEC.): 625-631.
|
23 |
SANTHOSH M, VENKAIAH C, KUMAR D. Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction [J]. Energy Conversion and Management, 2018, 168(15): 482-493.
|
24 |
TIAN Z. Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM[J]. Engineering Applications of Artificial Intelligence, 2020, 91(MAY.): 103573.
|
25 |
CHEN Zhuan, XIONG Xin, YOU Yuyin. Variational mode decomposition and long and short time neural network for dam deformation prediction[J]. Science of Surveying and Mapping, 2021, 49(9):34-42.
|
|
陈竹安,熊鑫,游宇垠. 变分模态分解与长短时神经网络的大坝变形预测[J]. 测绘科学,2021, 49(9):34-42.
|
26 |
LIU Yu, ZHANG Yufei, CHEN Shangqiao. Ultra-short-term wind speed prediction based on variational mode decomposition and bidirectional long short-term memory neural network[J]. Industrial Control Computer, 2020, 33(9): 54-57.
|
|
刘宇,张雨飞,陈尚巧. 基于变分模态分解与双向长短期记忆神经网络的超短期风速预测[J]. 工业控制计算机,2020,33(9):54-57.
|
27 |
ZHANG Y, PAN G, CHEN B, et al. Short-term wind speed prediction model based on GA-ANN improved by VMD[J]. Renewable Energy, 2020, 156(AUG.): 1 373-1 388.
|
28 |
Hu H L, Wang L, Tao R. Wind speed forecasting based on variational mode decomposition and improved echo state network[J]. Renewable Energy, 2021, 164(FEB.): 729-751.
|
29 |
LIU H, YANG R, WANG T, et al. A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections[J]. Renewable Energy, 2021, 165(1): 573-594.
|
30 |
ZHANG Y G, YUAN Z, KONG C H, et al. A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic[J]. Energy Conversion and Management, 2020, 203(1): 112254.
|
31 |
DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
|
32 |
YU Sheng, ZHOU Hongbo, YU Fan, et al. Application of Fuzzy Neural Network in power short-term load forecasting[J]. Power Grid Analysis & Study, 2018, 46(11): 88-91.
|
|
喻圣,邹红波,余凡,等. 模糊神经网络在电力系统短期负荷预测中的应用[J]. 电网分析与研究,2018,46(11):88-91.
|
33 |
TANG L H, BAI Y L, YANG J, et al. A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series[J]. Chaos, Solitons & Fractals, 2020, 141(DEC.): 110366.
|
34 |
YI Xuejun, WEI Shouke, SHI Yuhao, et al. Hydrological series prediction model based on wavelet nonlinear autoregressive network[J]. Computer Technology and Development, 2021, 31(3): 70-77.
|
|
衣学军,魏守科,石玉好,等. 基于小波非线性自回归网络的水文预测模型[J]. 计算机技术与发展,2021,31(3):70-77.
|
35 |
GUO Mingxin, HUANG Ruanming, BIAN Xiaoyan, et al. Shortterm wind speed time series forecasting method based on elman neural network and software development[J]. Industrial Control Computer, 2021, 34(2): 83-85.
|
|
郭明星,黄阮明,边晓燕,等. 基于Elman神经网络的短期风速时间序列预测及软件开发[J]. 工业控制计算机,2021,34(2):83-85.
|
36 |
DUAN J K, ZUO H C, BAI Y L, et al. Short-term wind speed forecasting using recurrent neural networks with error correction[J]. Energy, 2021, 217(15): 119397.
|
37 |
HE Y, LI J M, SUMEI R, et al. A hybrid model for financial time series forecasting-integration of EWT, ARIMA with the improved ABC optimized ELM[J]. IEEE Access, 2020, 8: 84 501-84 518.
|