{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T02:23:17Z","timestamp":1774318997491,"version":"3.50.1"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electrical Engineering"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.compeleceng.2026.111104","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T23:36:52Z","timestamp":1774309012000},"page":"111104","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Tri-DCNNet: A three-branch network with deep compensation for wind power forecasting"],"prefix":"10.1016","volume":"134","author":[{"given":"Hongliang","family":"Tian","sequence":"first","affiliation":[]},{"given":"Weichao","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Yinqiao","family":"Leng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.compeleceng.2026.111104_bib0001","doi-asserted-by":"crossref","first-page":"350","DOI":"10.3390\/en18020350","article-title":"A comprehensive review of wind power prediction based on machine learning: models, applications, and challenges","volume":"18","author":"Liu","year":"2025","journal-title":"Energies"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0002","doi-asserted-by":"crossref","first-page":"12753","DOI":"10.1007\/s00521-024-09923-4","article-title":"A survey on wind power forecasting with machine learning approaches","volume":"36","author":"Yang","year":"2024","journal-title":"Neural Comput Appl"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0003","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.egyr.2024.06.006","article-title":"A review of short-term wind power generation forecasting methods in recent technological trends","volume":"12","author":"Tuncar","year":"2024","journal-title":"Energy Rep"},{"issue":"4","key":"10.1016\/j.compeleceng.2026.111104_bib0004","doi-asserted-by":"crossref","first-page":"684","DOI":"10.3390\/forecast5040037","article-title":"Decompose and conquer: time series forecasting with multiseasonal trend decomposition using Loess","volume":"5","author":"Sohrabbeig","year":"2023","journal-title":"Forecasting"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0005","article-title":"Ultra-short-term wind power forecasting method based on optimized decomposition and deep learning","volume":"28","author":"Shu","year":"2025","journal-title":"Energy Convers Manag: X"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113110","article-title":"Adaptive parameter selection variational mode decomposition based on a novel hybrid entropy","volume":"217","author":"Xu","year":"2023","journal-title":"Measurement"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.117766","article-title":"A review of wind speed and wind power forecasting with deep neural networks","volume":"304","author":"Wang","year":"2021","journal-title":"Appl Energy"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0008","series-title":"International Conference on Learning Representations (ICLR)","article-title":"TimesNet: temporal 2D-variation modeling for general time series analysis","author":"Wu","year":"2022"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.124068","article-title":"Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events","volume":"376","author":"Liu","year":"2024","journal-title":"Appl Energy"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2023.138676","article-title":"A new distributed decomposition\u2013reconstruction\u2013ensemble learning paradigm for short-term wind power prediction","volume":"423","author":"Zhao","year":"2023","journal-title":"J Clean Prod"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0011","article-title":"Short-term wind power forecasting using wavelet packet decomposition and LSTM neural network with attention mechanism","volume":"109","author":"Jiang","year":"2023","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0012","article-title":"Physics-informed deep learning for wind power forecasting with operational constraints","volume":"112","author":"Chen","year":"2024","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0013","article-title":"A hybrid model for nonstationary wind power time series forecasting based on CEEMDAN and improved Transformer","volume":"102","author":"Li","year":"2022","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2022.107886","article-title":"A wind power forecasting method based on optimized decomposition prediction and error correction","volume":"208","author":"Li","year":"2022","journal-title":"Electr Power Syst Res"},{"issue":"5","key":"10.1016\/j.compeleceng.2026.111104_bib0015","doi-asserted-by":"crossref","first-page":"520","DOI":"10.3390\/e23050520","article-title":"Application of parameter-optimized VMD method in fault feature extraction","volume":"23","author":"Liang","year":"2021","journal-title":"Entropy"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0016","first-page":"5409","article-title":"A robust seasonal-trend decomposition algorithm for long time series","volume":"33","author":"Wen","year":"2019","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0017","article-title":"Multi-scale feature fusion for wind power forecasting using CNN and BiLSTM with adaptive optimization","volume":"107","author":"Wang","year":"2023","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0018","article-title":"Robust wind power forecasting under data missing scenarios using generative adversarial networks","volume":"118","author":"Zhao","year":"2025","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0019","article-title":"Component-wise decoupling and compensation for wind power forecasting: a deep learning approach","volume":"113","author":"Zhang","year":"2024","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.compeleceng.2026.111104_bib51","series-title":"Are transformers effective for time series forecasting? Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11121","author":"Zeng","year":"2023"},{"issue":"8","key":"10.1016\/j.compeleceng.2026.111104_bib52","doi-asserted-by":"crossref","first-page":"3777","DOI":"10.1109\/TNNLS.2020.3015971","article-title":"Density encoding enables resource-efficient randomly connected neural networks","volume":"32","author":"Kleyko","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.compeleceng.2026.111104_bib53","doi-asserted-by":"crossref","first-page":"122260","DOI":"10.1016\/j.ins.2025.122260","article-title":"A mirrored echo state network with application to time series prediction","volume":"716","author":"Chen","year":"2025","journal-title":"Inf Sci"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0020","article-title":"Ultra-short-term wind power forecasting method based on optimized decomposition and deep learning","volume":"28","author":"Shu","year":"2025","journal-title":"Energy Convers Manag: X"},{"issue":"7","key":"10.1016\/j.compeleceng.2026.111104_bib0021","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.3390\/pr13072046","article-title":"Multi-scale decomposition and hybrid deep learning CEEMDAN-VMD-CNN-BiLSTM approach for wind power forecasting","volume":"13","author":"Ning","year":"2025","journal-title":"Processes"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.renene.2023.119357","article-title":"Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method","volume":"218","author":"Karijadi","year":"2023","journal-title":"Renew Energy"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0023","article-title":"SMGformer: integrating STL and multi-head self-attention in deep learning model for multi-step runoff forecasting","volume":"14","author":"Wang","year":"2024","journal-title":"Sci Rep"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2024.114349","article-title":"Wind power forecasting system with data enhancement and algorithm improvement","volume":"196","author":"Zhang","year":"2024","journal-title":"Renew Sustain Energy Rev"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109333","article-title":"Amercing: an intuitive and effective constraint for dynamic time warping","volume":"137","author":"Herrmann","year":"2023","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0026","article-title":"A PatchTST-GRU based heterogeneous seq2seq model with NWP refinement for multi-step wind power forecasting","volume":"15","author":"Xu","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0027","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10012","article-title":"Swin Transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109029","article-title":"Multi-scale multi-hierarchy attention convolutional neural network for fetal brain extraction","volume":"133","author":"Sun","year":"2023","journal-title":"Pattern Recognit"},{"issue":"12","key":"10.1016\/j.compeleceng.2026.111104_bib0029","doi-asserted-by":"crossref","first-page":"12497","DOI":"10.1109\/TKDE.2023.3277839","article-title":"Channel attention for sensor-based activity recognition: embedding features into all frequencies in DCT Domain","volume":"35","author":"Xu","year":"2023","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0030","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2024.100442","article-title":"A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction","volume":"18","author":"Li","year":"2024","journal-title":"Energy AI"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.129171","article-title":"Short-term wind power forecasting model based on temporal convolutional network and Informer","volume":"283","author":"Gong","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.compeleceng.2026.111104_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113460","article-title":"Multilevel probabilistic wind power forecasting using an adaptive Informer network","volume":"180","author":"Xie","year":"2025","journal-title":"Appl Soft Comput"}],"container-title":["Computers and Electrical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S004579062600176X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S004579062600176X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:45:48Z","timestamp":1774316748000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S004579062600176X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":36,"alternative-id":["S004579062600176X"],"URL":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111104","relation":{},"ISSN":["0045-7906"],"issn-type":[{"value":"0045-7906","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Tri-DCNNet: A three-branch network with deep compensation for wind power forecasting","name":"articletitle","label":"Article Title"},{"value":"Computers and Electrical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111104","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111104"}}