Wind Speed Forecasting System Based on Variational Mode Decomposition and the Optimal Machine Learning Models
Received date: 2021-05-20
Revised date: 2021-08-01
Online published: 2021-10-15
Supported by
the National Natural Science Foundation "Research on observation error of data assimilation based on scale space theory and geostatistics"(41861047)
Due to the limited reserves of traditional energy and the excessive use of non-renewable energy， the global carbon emissions seriously accelerate the global greenhouse effect. Compared with traditional energy， wind energy is a kind of green renewable energy. Accurate wind speed prediction is of great significance to wind power generation system， but the inherent randomness of wind speed signal makes its fluctuation complex and uncontrollable. It is difficult to grasp the characteristics of wind speed series using single or fixed hybrid models in previous studies. Therefore， a wind speed forecasting system is proposed based on decomposition and the optimal machine learning models. First， the Variational Mode Decomposition （VMD） algorithm is used to decompose the original wind speed sequence into several sub-sequences. Then， five models of Fuzzy Neural Network （FNN）， Nonlinear Auto-regressive Neural Network （NARX）， Elman neural network， Back Propagation Neural Network （BPNN） and Auto-regressive integrated Moving Average model （ARIMA）， are selected to construct a machine learning model selection system and predict the validation set of sub-sequences， respectively. The root mean square error （RMSE） is used to select the optimal model and the final prediction result is obtained by summing up the predicted values of each sub-sequence. The measured wind speed data from four stations in Ningxia Hui Autonomous Region was used in the experiment. The sampling height was 70 meters and the time interval was 15 minutes. Three groups of comparison models were established to verify the prediction effect of the proposed model. The first group： five single models used in this experiment： FNN， NARX， Elman， BPNN and ARIMA； The second group： five hybrid models based on VMD decomposition： VMD-FNN， VMD-NARX， VMD-Elman， VMD-BPNN and VMD-ARIMA； The third group： newer hybrid models of deep learning： Improved Complete Ensemble Empirical Mode Decomposition （ICEEMDAN）-Long Short-Term Memory （LSTM） and ICEEMDAN-Gated Recurrent Unit （GRU）. The experimental results indicate that the proposed model is superior to other baseline models. The VMD decomposition method can effectively reduce the complexity of the original wind speed sequence. For different sub-sequences after decomposition， the optimal method of machine learning model can capture the sequence features and obtain the optimal prediction results.
Yulong BAI , Yani LU , Mingde LIU . Wind Speed Forecasting System Based on Variational Mode Decomposition and the Optimal Machine Learning Models[J]. Advances in Earth Science, 2021 , 36(9) : 937 -949 . DOI: 10.11867/j.issn.1001-8166.2021.090
| 1 | IRENA. Wind energy ［EB/OL］. 2021. . |
| 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. |
| 4 | 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. |
| 6 | 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. |
| 9 | 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. |
| 20 | 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. |
| 25 | 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. |
| 26 | 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. |
| 32 | 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. |
| 34 | 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. |
| 35 | 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. |
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