{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:24:53Z","timestamp":1774535093658,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T00:00:00Z","timestamp":1664409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power development. To promote the utilization of wind energy and improve the accuracy of wind power prediction, we comprehensively consider the influence of wind farm environmental factors and historical power on wind power generation. This paper presents a short-term wind power prediction model based on time convolution neural network (TCN) and variational mode decomposition (VMD). First, due to the non-smooth characteristics of the wind farm environmental data, this paper uses VMD to decompose the data of each environmental variable to reduce the influence of the random noise of the data on the prediction model. Then, the modal components with rich feature information are extracted according to the Pearson correlation coefficient and Maximal information coefficient (MIC) between each modal component and the power. Thirdly, a prediction model based on TCN is trained according to the preferred modal components and historical power data to achieve accurate short-term wind power prediction. In this paper, the model is trained and tested with a public wind power dataset provided by the Spanish Power Company. The simulation results show that the model has higher prediction accuracy, with MAPE and R2 are 2.79% and 0.9985, respectively. Compared with the conventional long short-term neural network (LSTM) model, the model in this paper has good prediction accuracy and robustness.<\/jats:p>","DOI":"10.3390\/s22197414","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T23:09:29Z","timestamp":1664492969000},"page":"7414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6038-346X","authenticated-orcid":false,"given":"Jingwei","family":"Tang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hunan College of Information, Changsha 410200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3013-0290","authenticated-orcid":false,"given":"Ying-Ren","family":"Chien","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Ilan University, Yilan 260007, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"ref_1","first-page":"243","article-title":"Probability box theory-based uncertain power flow calculation for power system with wind power","volume":"22","author":"Ding","year":"2021","journal-title":"Int. J. Emerg. Electr. Power Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"120000","DOI":"10.1016\/j.energy.2021.120000","article-title":"Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources","volume":"223","author":"Yun","year":"2021","journal-title":"Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TSTE.2021.3109044","article-title":"Wind Power Curve Modeling with Hybrid Copula and Grey Wolf Optimization","volume":"13","author":"Wei","year":"2022","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"110504","DOI":"10.1016\/j.rser.2020.110504","article-title":"Profit-based unit commitment problem: A review of models, methods, challenges, and future directions","volume":"138","author":"Abdi","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5355","DOI":"10.1109\/JSYST.2021.3079584","article-title":"Investigating the Need for Real-Time Adjustment Cost in Unit Commitment Framework for Wind-Integrated Power Systems","volume":"15","author":"Ranjan","year":"2021","journal-title":"IEEE Syst. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.neucom.2021.07.084","article-title":"Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm","volume":"462","author":"Dong","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1016\/j.renene.2020.10.119","article-title":"Wind power forecasting\u2014A data-driven method along with gated recurrent neural network","volume":"163","author":"Kisvari","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106822","DOI":"10.1016\/j.ast.2021.106822","article-title":"A novel hybrid method for flight departure delay prediction using Random Forest Regression and Maximal Information Coefficient","volume":"116","author":"Guo","year":"2021","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_9","first-page":"100601","article-title":"Short-term wind speed forecasting framework based on stacked denoising auto-encoders with rough ANN","volume":"38","author":"Jahangir","year":"2020","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8298","DOI":"10.1109\/TIE.2020.3009604","article-title":"Deep Learning-Based Forecasting Approach in Smart Grids with Microclustering and Bidirectional LSTM Network","volume":"68","author":"Jahangir","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107452","DOI":"10.1016\/j.ijepes.2021.107452","article-title":"A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting","volume":"134","author":"Jiandong","year":"2022","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/TCST.2021.3056751","article-title":"Distributed Demand Side Management with Stochastic Wind Power Forecasting","volume":"30","author":"Scarabaggio","year":"2022","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112824","DOI":"10.1016\/j.enconman.2020.112824","article-title":"A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets","volume":"213","author":"Memarzadeh","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2586","DOI":"10.1109\/TIA.2022.3146224","article-title":"Combined Approach for Short-Term Wind Power Forecasting Based on Wave Division and Seq2Seq Model Using Deep Learning","volume":"58","author":"Ye","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"121505","DOI":"10.1016\/j.jclepro.2020.121505","article-title":"Study on environment-concerned short-term load forecasting model for wind power based on feature extraction and tree regression","volume":"264","author":"Liu","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_16","first-page":"1242","article-title":"Short term power load forecasting based on time convolution network%","volume":"37","author":"Yang","year":"2022","journal-title":"J. Electr. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cai, C., Li, Y., Su, Z., Zhu, T., and He, Y. (2022). Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network. Appl. Sci., 12.","DOI":"10.3390\/app12136647"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ding, J., Huang, L., Xiao, D., and Li, X. (2020). GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction. Sensors, 20.","DOI":"10.3390\/s20071946"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.ymssp.2018.06.055","article-title":"Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable working conditions","volume":"116","author":"Li","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111060","DOI":"10.1016\/j.measurement.2022.111060","article-title":"Online chatter detection in milling process based on fast iterative VMD and energy ratio difference","volume":"194","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1177\/0957650919885720","article-title":"Fault feature extraction of wind turbine gearbox under variable speed based on improved adaptive variational mode decomposition","volume":"234","author":"Zheng","year":"2020","journal-title":"Proc. Inst. Mech. Eng. Part A-J. Power Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational Mode Decomposition","volume":"62","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_23","unstructured":"Bai, J.Z.K.S., and Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. presented at the CVPR. arXiv, preprint."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110784","DOI":"10.1016\/j.jcp.2021.110784","article-title":"A deep learning framework for constitutive modeling based on temporal convolutional network","volume":"449","author":"Wang","year":"2022","journal-title":"J. Comput. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"20493","DOI":"10.1109\/JSEN.2021.3096215","article-title":"Quality Variable Prediction for Nonlinear Dynamic Industrial Processes Based on Temporal Convolutional Networks","volume":"21","author":"Yuan","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"920837","DOI":"10.3389\/fenrg.2022.920837","article-title":"SCADA Data Based Wind Power Interval Prediction Using LUBE-Based Deep Residual Networks","volume":"10","author":"Li","year":"2022","journal-title":"Front. Energy Res."},{"key":"ref_27","first-page":"71","article-title":"Short-term power load forecasting based on SSA-LSTM model","volume":"41","author":"Zhao","year":"2022","journal-title":"New Technol. Electr. Power"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1002\/we.2613","article-title":"Short-term wind speed multistep combined forecasting model based on two-stage decomposition and LSTM","volume":"24","author":"Liao","year":"2021","journal-title":"Wind Energy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101433","DOI":"10.1016\/j.aei.2021.101433","article-title":"Research on hybrid feature selection method of power transformer based on fuzzy information entropy","volume":"50","author":"Yu","year":"2021","journal-title":"Adv. Eng. Inform."},{"key":"ref_30","first-page":"1740","article-title":"A single pole ground fault protection scheme for MMC multi-terminal flexible DC distribution network based on transient voltage Pearson correlation","volume":"46","author":"Shangguan","year":"2020","journal-title":"High Volt. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:41:55Z","timestamp":1760143315000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,29]]},"references-count":30,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197414"],"URL":"https:\/\/doi.org\/10.3390\/s22197414","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,29]]}}}