{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:09:56Z","timestamp":1784012996915,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819228676","type":"print"},{"value":"9789819228683","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-2868-3_6","type":"book-chapter","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T06:59:01Z","timestamp":1784012341000},"page":"73-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Wind Power Forecasting via\u00a0Adversarial State-Space Modeling and\u00a0Dynamic Mode Decomposition"],"prefix":"10.1007","author":[{"given":"Tao","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiya","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Gupta, A., Sharma, K.C., Vijayvargia, A., Bhakar, R.: Very short term wind power prediction using hybrid univariate arima-garch model. In: 2019 8th International Conference on Power Systems (ICPS), pp.\u00a01\u20136. IEEE (2019)","DOI":"10.1109\/ICPS48983.2019.9067611"},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"136254","DOI":"10.1109\/ACCESS.2019.2942012","volume":"7","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Sun, H., Guo, Y.: Wind power prediction based on PSO-SVR and grey combination model. IEEE Access 7, 136254\u2013136267 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"6_CR3","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1002\/for.2889","volume":"42","author":"K Ghasvarian Jahromi","year":"2023","unstructured":"Ghasvarian Jahromi, K., Gharavian, D., Mahdiani, H.R.: Wind power prediction based on wind speed forecast using hidden Markov model. J. Forecast. 42(1), 101\u2013123 (2023)","journal-title":"J. Forecast."},{"issue":"1","key":"6_CR4","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1109\/TIA.2021.3127145","volume":"58","author":"M Li","year":"2021","unstructured":"Li, M., Yang, M., Yu, Y., Lee, W.J.: A wind speed correction method based on modified hidden Markov model for enhancing wind power forecast. IEEE Trans. Ind. Appl. 58(1), 656\u2013666 (2021)","journal-title":"IEEE Trans. Ind. Appl."},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Geng, D., Ma, J., Zhao, Y.: Electricity generation forecasting with wind power data: a grey model approach. In: 2021 2nd International Conference on Artificial Intelligence and Information Systems, pp.\u00a01\u20136 (2021)","DOI":"10.1145\/3469213.3469230"},{"issue":"6","key":"6_CR6","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.1177\/0309524X221113013","volume":"46","author":"SM Malakouti","year":"2022","unstructured":"Malakouti, S.M., Ghiasi, A.R., Ghavifekr, A.A., Emami, P.: Predicting wind power generation using machine learning and CNN-LSTM approaches. Wind Eng. 46(6), 1853\u20131869 (2022)","journal-title":"Wind Eng."},{"issue":"13","key":"6_CR7","doi-asserted-by":"publisher","first-page":"2469","DOI":"10.3390\/electronics13132469","volume":"13","author":"X Huo","year":"2024","unstructured":"Huo, X., et al.: Research of short-term wind power generation forecasting based on MRMR-PSO-LSTM algorithm. Electronics 13(13), 2469 (2024)","journal-title":"Electronics"},{"issue":"2","key":"6_CR8","doi-asserted-by":"publisher","first-page":"2586","DOI":"10.1109\/TIA.2022.3146224","volume":"58","author":"L Ye","year":"2022","unstructured":"Ye, L., et al.: Combined approach for short-term wind power forecasting based on wave division and seq2seq model using deep learning. IEEE Trans. Ind. Appl. 58(2), 2586\u20132596 (2022)","journal-title":"IEEE Trans. Ind. Appl."},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Liu, S., Xiao, S., Liu, L., Liu, J.: Wind power prediction based on meteorological data visualization. In: Proceedings of the 8th EAI International Conference on Green Energy and Networking (GreeNets 2021), p.\u00a01 (2021)","DOI":"10.4108\/eai.6-6-2021.2307765"},{"key":"6_CR10","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1016\/j.egyr.2021.10.102","volume":"8","author":"B He","year":"2022","unstructured":"He, B., et al.: A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data. Energy Rep. 8, 929\u2013939 (2022)","journal-title":"Energy Rep."},{"key":"6_CR11","unstructured":"Bhat, S.: Mathematical formalism for memory compression in selective state space models. arXiv preprint arXiv:2410.03158 (2024)"},{"key":"6_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111279","volume":"159","author":"BN Patro","year":"2025","unstructured":"Patro, B.N., Agneeswaran, V.S.: Mamba-360: survey of state space models as transformer alternative for long sequence modelling: methods, applications, and challenges. Eng. Appl. Artif. Intell. 159, 111279 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Gao, X., Chen, N.F.: Speech-mamba: long-context speech recognition with selective state spaces models. In: 2024 IEEE Spoken Language Technology Workshop (SLT), pp.\u00a01\u20136. IEEE (2024)","DOI":"10.1109\/SLT61566.2024.10832137"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Wu, H., Yang, Y., Xu, H., Wang, W., Zhou, J., Zhu, L.: Rainmamba: enhanced locality learning with state space models for video deraining. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp. 7881\u20137890 (2024)","DOI":"10.1145\/3664647.3680916"},{"key":"6_CR15","unstructured":"Cai, X., Zhu, Y., Wang, X., Yao, Y.: Mambats: improved selective state space models for long-term time series forecasting. arXiv preprint arXiv:2405.16440 (2024)"},{"key":"6_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.119692","volume":"220","author":"X Peng","year":"2021","unstructured":"Peng, X., et al.: Ealstm-qr: interval wind-power prediction model based on numerical weather prediction and deep learning. Energy 220, 119692 (2021)","journal-title":"Energy"},{"key":"6_CR17","doi-asserted-by":"publisher","first-page":"1336675","DOI":"10.3389\/fenrg.2023.1336675","volume":"11","author":"J Li","year":"2024","unstructured":"Li, J., Liu, M., Wen, L.: Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction. Front. Energy Res. 11, 1336675 (2024)","journal-title":"Front. Energy Res."},{"key":"6_CR18","first-page":"786","volume":"126","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Li, J., Zhang, N.: Short-term wind power forecasting based on LSTM-GAN. Renew. Energy 126, 786\u2013794 (2018)","journal-title":"Renew. Energy"},{"issue":"3","key":"6_CR19","first-page":"1987","volume":"11","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Wang, X., Chen, B.: Wind farm cluster power forecasting using CNN-GAN with spatial-temporal correlation. IEEE Trans. Sustainable Energy 11(3), 1987\u20131998 (2020)","journal-title":"IEEE Trans. Sustainable Energy"},{"key":"6_CR20","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.117068","volume":"296","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Liu, C., Sun, Y.: Conditional GAN-based wind power forecasting with meteorological constraint. Appl. Energy 296, 117062 (2021)","journal-title":"Appl. Energy"},{"issue":"2","key":"6_CR21","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1109\/TSG.2022.3200704","volume":"14","author":"M Zhao","year":"2023","unstructured":"Zhao, M., Li, D., Xu, C.: Multi-modal GAN for wind power forecasting: fusing satellite imagery and NWP data. IEEE Trans. Smart Grid 14(2), 1563\u20131574 (2023)","journal-title":"IEEE Trans. Smart Grid"},{"key":"6_CR22","unstructured":"Zhou, J., et al.: Sdwpf: a dataset for spatial dynamic wind power forecasting challenge at kdd cup 2022. arXiv preprint arXiv:2208.04360 (2022)"},{"key":"6_CR23","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural. Inf. Process. Syst. 34, 22419\u201322430 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"6_CR24","unstructured":"Liu, Y., et al.: itransformer: inverted transformers are effective for time series forecasting. In: International Conference on Learning Representations (ICLR) (2024)"},{"issue":"3","key":"6_CR25","first-page":"1","volume":"16","author":"X Liang","year":"2024","unstructured":"Liang, X., Gu, Q., You, X.: Wpformer: a spatial-temporal graph transformer with auto-correlation for wind power forecasting. IEEE Trans. Sustain. Energy 16(3), 1\u201313 (2024)","journal-title":"IEEE Trans. Sustain. Energy"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2868-3_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T06:59:05Z","timestamp":1784012345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2868-3_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"ISBN":["9789819228676","9789819228683"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2868-3_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]},"assertion":[{"value":"15 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem2026.rosc.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}