{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:48:28Z","timestamp":1772552908796,"version":"3.50.1"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031734991","type":"print"},{"value":"9783031735004","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73500-4_17","type":"book-chapter","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T03:59:43Z","timestamp":1731643183000},"page":"197-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Generative Adversarial Networks for\u00a0Synthetic Meteorological Data Generation"],"prefix":"10.1007","author":[{"given":"Diogo","family":"Viana","sequence":"first","affiliation":[]},{"given":"Rita","family":"Teixeira","sequence":"additional","affiliation":[]},{"given":"Tiago","family":"Soares","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Baptista","sequence":"additional","affiliation":[]},{"given":"Tiago","family":"Pinto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"key":"17_CR1","volume-title":"Local Electricity Markets","year":"2021","unstructured":"Pinto, T., Vale, Z., Widergren, S. (eds.): Local Electricity Markets. Elsevier, Amsterdam (2021)"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Mohammadi, A., Chumachenko, D.: Machine learning model of COVID-19 forecasting in Ukraine based on the linear regression. In: 2021 IEEE 12th International Conference on Electronics and Information Technologies (ELIT) (2021)","DOI":"10.1109\/ELIT53502.2021.9501122"},{"issue":"1","key":"17_CR3","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Maga. 35(1), 53\u201365 (2018)","journal-title":"IEEE Signal Process. Maga."},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Libes, D., Lechevalier, D., Jain, S.: Issues in synthetic data generation for advanced manufacturing. In: 2017 IEEE International Conference on Big Data (Big Data) (2017)","DOI":"10.1109\/BigData.2017.8258117"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Endres, M., Venugopal, A.M., Tran, T.S.: Synthetic data generation: a comparative study. In: Proceedings of the 26th International Database Engineered Applications Symposium (2022)","DOI":"10.1145\/3548785.3548793"},{"key":"17_CR6","doi-asserted-by":"publisher","first-page":"5769","DOI":"10.3390\/app14135769","volume":"14","author":"R Teixeira","year":"2024","unstructured":"Teixeira, R., Cerveira, A., Pires, E.J.S., Baptista, J.: Enhancing weather forecasting integrating LSTM and GA. Appl. Sci. 14, 5769 (2024)","journal-title":"Appl. Sci."},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"83286","DOI":"10.1109\/ACCESS.2019.2922692","volume":"7","author":"J Yang","year":"2019","unstructured":"Yang, J., Li, T., Liang, G., He, W., Zhao, Y.: A simple recurrent unit model based intrusion detection system with DCGAN. IEEE Access 7, 83286\u201383296 (2019)","journal-title":"IEEE Access"},{"issue":"5","key":"17_CR8","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.3390\/s23052799","volume":"23","author":"Q Li","year":"2023","unstructured":"Li, Q., Zhang, D., Yan, K.: A solar irradiance forecasting framework based on the CEE-WGAN-LSTM model. Sensors 23(5), 2799 (2023)","journal-title":"Sensors"},{"key":"17_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2021.651807","volume":"9","author":"C Wang","year":"2021","unstructured":"Wang, C., Cao, Y., Zhang, S., Ling, T.: A reconstruction method for missing data in power system measurement based on LSGAN. Front. Energy Res. 9, 651807 (2021)","journal-title":"Front. Energy Res."},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Gonog, L., Zhou, Y.: A review: generative adversarial networks. In: 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) (2019)","DOI":"10.1109\/ICIEA.2019.8833686"},{"key":"17_CR11","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.neucom.2019.12.032","volume":"384","author":"Y Yu","year":"2020","unstructured":"Yu, Y., Huang, Z., Li, F., Zhang, H., Le, X.: Point encoder GAN: a deep learning model for 3D point cloud inpainting. Neurocomputing 384, 192\u2013199 (2020)","journal-title":"Neurocomputing"},{"key":"17_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.segan.2023.101157","volume":"36","author":"B Yilmaz","year":"2023","unstructured":"Yilmaz, B.: A scenario framework for electricity grid using generative adversarial networks. Sustain. Energy Grids Netw. 36, 101157 (2023)","journal-title":"Sustain. Energy Grids Netw."},{"key":"17_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2023.119374","volume":"219","author":"J Liu","year":"2023","unstructured":"Liu, J., et al.: A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation. Renew. Energy 219, 119374 (2023)","journal-title":"Renew. Energy"},{"key":"17_CR14","unstructured":"Ip Patrim\u00f3nio. https:\/\/www.ippatrimonio.pt\/pt-pt\/estacoes\/estacao-do-pinhao. Accessed 27 May 2024"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Kingma, D., Welling, M.: An introduction to variational autoencoders. Found. Trends $$\\text{\\textregistered} $$ Mach. Learn. 12, 307\u2013392 (2019)","DOI":"10.1561\/2200000056"},{"key":"17_CR16","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73500-4_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T17:19:00Z","timestamp":1743182340000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73500-4_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,16]]},"ISBN":["9783031734991","9783031735004"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73500-4_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,16]]},"assertion":[{"value":"16 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Viana do Castelo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2024.pt","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}