{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T09:16:45Z","timestamp":1759483005049,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030788100"},{"type":"electronic","value":"9783030788117"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78811-7_26","type":"book-chapter","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:22:37Z","timestamp":1625613757000},"page":"272-281","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Hybrid Wind Speed Prediction Model Based on Signal Decomposition and Deep 1DCNN"],"prefix":"10.1007","author":[{"given":"Yuhui","family":"Wang","sequence":"first","affiliation":[]},{"given":"Qingjian","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chenxin","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"issue":"6","key":"26_CR1","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1016\/j.renene.2009.10.028","volume":"35","author":"L Lazi\u0107","year":"2010","unstructured":"Lazi\u0107, L., Pejanovi\u0107, G., \u017divkovi\u0107, M.: Wind forecasts for wind power generation using the eta model. Renew. Energy 35(6), 1236\u20131243 (2010)","journal-title":"Renew. Energy"},{"doi-asserted-by":"crossref","unstructured":"Zhang, L.L., Li, M.S., Ji, T.Y., Wu, Q.H.: Short-term wind power prediction based on intrinsic time-scale decomposition and ls-svm. In: The IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), Melbourne, Asia, pp. 41\u201345 (2016)","key":"26_CR2","DOI":"10.1109\/ISGT-Asia.2016.7796358"},{"doi-asserted-by":"crossref","unstructured":"Han, L., Zhang, R., Wang, X., Bao, A., Jing, H.: Multi-step wind power forecast based on VMD-LSTM. IET Renew. Power Gener. 13, 1690\u20131700(10) (2019)","key":"26_CR3","DOI":"10.1049\/iet-rpg.2018.5781"},{"doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.C., Yang, Y., Liu, H.: Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, USA, pp. 95\u2013104 (2018)","key":"26_CR4","DOI":"10.1145\/3209978.3210006"},{"unstructured":"Bai, S., Zico Kolter, J., Koltun, V.: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv e-prints arXiv:1803.01271 (2018)","key":"26_CR5"},{"key":"26_CR6","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1016\/j.enconman.2017.05.063","volume":"148","author":"C Yu","year":"2017","unstructured":"Yu, C., Li, Y., Zhang, M.: An improved wavelet transform using singular spectrum analysis for wind speed forecasting based on elman neural network. Energy Conv. Manag. 148, 895\u2013904 (2017)","journal-title":"Energy Conv. Manag."},{"doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 1746\u20131751 (2014)","key":"26_CR7","DOI":"10.3115\/v1\/D14-1181"},{"issue":"7","key":"26_CR8","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, J., Chao, H., Yang, M.: Image blind denoising with generative adversarial network based noise modeling. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, US (2018)","key":"26_CR9","DOI":"10.1109\/CVPR.2018.00333"},{"key":"26_CR10","doi-asserted-by":"publisher","first-page":"63868","DOI":"10.1109\/ACCESS.2019.2915544","volume":"7","author":"A Koochali","year":"2019","unstructured":"Koochali, A., Schichtel, P., Dengel, A., Ahmed, S.: Probabilistic forecasting of sensory data with generative adversarial networks - forgan. IEEE Access 7, 63868\u201363880 (2019)","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Xu, Z., Du, J., Wang, J., Jiang, C., Ren, Y.: Satellite image prediction relying on GAN and LSTM neural networks. In: The IEEE International Conference on Communications (ICC), Shanghai, China, pp. 1\u20136 (2019)","key":"26_CR11","DOI":"10.1109\/ICC.2019.8761462"},{"unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, vol. 70, pp. 214\u2013223 (2017)","key":"26_CR12"},{"doi-asserted-by":"crossref","unstructured":"Pascual, S., Bonafonte, A., Serr\u00e0, J.: Segan: speech enhancement generative adversarial network. In: Conference of the International Speech Communication Association (INTERSPEECH), Stockholm, Sweden, pp. 3642\u20133646 (2017)","key":"26_CR13","DOI":"10.21437\/Interspeech.2017-1428"},{"key":"26_CR14","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.enconman.2018.11.006","volume":"180","author":"X Mi","year":"2019","unstructured":"Mi, X., Liu, H., Li, Y.: Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine. Energy Conv. Manag. 180, 196\u2013205 (2019)","journal-title":"Energy Conv. Manag."},{"doi-asserted-by":"crossref","unstructured":"Torres, M.E., Colominas, M.A., Schlotthauer, G., Flandrin, P.: A complete ensemble empirical mode decomposition with adaptive noise. In: The IEEE International Conference on Acoustics, Speech and Signal Processing, Czech Republic, Prague, pp. 4144\u20134147 (2011)","key":"26_CR15","DOI":"10.1109\/ICASSP.2011.5947265"}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78811-7_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:24:05Z","timestamp":1625613845000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78811-7_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030788100","9783030788117"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78811-7_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iasei.org\/icsi2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"104","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4-5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}