{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:21:25Z","timestamp":1765502485011,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761543","type":"proceedings-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T01:03:42Z","timestamp":1762563822000},"page":"6093-6101","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SolarMAE: A Unified framework for Regional Centralized and Distributed Solar Power Forecasting with Weather Pre-training"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1139-7071","authenticated-orcid":false,"given":"Jin","family":"Wang","sequence":"first","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7515-897X","authenticated-orcid":false,"given":"Bingqing","family":"Peng","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2292-7141","authenticated-orcid":false,"given":"Wenwei","family":"Wang","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0707-788X","authenticated-orcid":false,"given":"Yuanjie","family":"Hu","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2545-9440","authenticated-orcid":false,"given":"Yuejiang","family":"Chen","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7023-0900","authenticated-orcid":false,"given":"Peisong","family":"Niu","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5835-7259","authenticated-orcid":false,"given":"Liang","family":"Sun","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Alibaba Cloud. 2023. Object Storage Service (OSS). https:\/\/www.alibabacloud.com\/product\/oss Accessed: 2023--10--15."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"e_1_3_2_1_3_1","volume-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271","author":"Bai Shaojie","year":"2018","unstructured":"Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)."},{"key":"e_1_3_2_1_4_1","volume-title":"The quiet revolution of numerical weather prediction. Nature","author":"Bauer A. Brunet G.","year":"2015","unstructured":"Thorpe-A. Brunet G. Bauer, P. 2015. The quiet revolution of numerical weather prediction. Nature (2015), 525."},{"key":"e_1_3_2_1_5_1","volume-title":"Emerging Properties in Self-Supervised Vision Transformers. CoRR abs\/2104.14294","author":"Caron Mathilde","year":"2021","unstructured":"Mathilde Caron, Hugo Touvron, Ishan Misra, Herv\u00e9 J\u00e9gou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. 2021. Emerging Properties in Self-Supervised Vision Transformers. CoRR abs\/2104.14294 (2021). arXiv:2104.14294 https:\/\/arxiv.org\/abs\/2104.14294"},{"key":"e_1_3_2_1_6_1","volume-title":"Tsmixer: An all-mlp architecture for time series forecasting. arXiv preprint arXiv:2303.06053","author":"Chen Si-An","year":"2023","unstructured":"Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan O Arik, and Tomas Pfister. 2023. Tsmixer: An all-mlp architecture for time series forecasting. arXiv preprint arXiv:2303.06053 (2023)."},{"key":"e_1_3_2_1_7_1","volume-title":"Hinton","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. CoRR abs\/2002.05709 (2020). arXiv:2002.05709 https:\/\/arxiv.org\/abs\/2002.05709"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Minneapolis, MN, USA, June 2--7, 2019. 4171--4186."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2019.05.073"},{"key":"e_1_3_2_1_10_1","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"e_1_3_2_1_11_1","unstructured":"Christoph Feichtenhofer Yanghao Li Kaiming He et al. 2022. Masked autoencoders as spatiotemporal learners. Advances in neural information processing systems 35 (2022) 35946--35958."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.07.168"},{"key":"e_1_3_2_1_13_1","volume-title":"Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R\u00e9mi Munos, and Michal Valko.","author":"Grill Jean-Bastien","year":"2020","unstructured":"Jean-Bastien Grill, Florian Strub, Florent Altch\u00e9, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo \u00c1vila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R\u00e9mi Munos, and Michal Valko. 2020. Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. CoRR abs\/2006.07733 (2020). arXiv:2006.07733 https:\/\/arxiv.org\/abs\/2006.07733"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"e_1_3_2_1_15_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2921238"},{"key":"e_1_3_2_1_17_1","unstructured":"Paris IEA. [n.d.]. Renewables 2024. https:\/\/www.iea.org\/reports\/renewables- 2024 Licence: CC BY 4.0."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2014.02.018"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2024.114479"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.129716"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","unstructured":"Chengxi Li Jinfeng Huang Xiaobing Wu Jianing Huang Yi Liu and Jun He. 2023. Spatial Correlation-Based Ultra-Short-Term Power Generation Prediction of Grid-connected Distributed PV in Counties. In 2023 8th International Conference on Power and Renewable Energy (ICPRE). 1807--1813. doi:10.1109\/ICPRE59655. 2023.10353817","DOI":"10.1109\/ICPRE59655"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2023.3236992"},{"key":"e_1_3_2_1_23_1","volume-title":"itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625","author":"Liu Yong","year":"2023","unstructured":"Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. 2023. itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625 (2023)."},{"key":"e_1_3_2_1_24_1","volume-title":"W-mae: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting. arXiv preprint arXiv:2304.08754","author":"Man Xin","year":"2023","unstructured":"Xin Man, Chenghong Zhang, Jin Feng, Changyu Li, and Jie Shao. 2023. W-mae: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting. arXiv preprint arXiv:2304.08754 (2023)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2011.02.013"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2017.04.066"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.120989"},{"key":"e_1_3_2_1_28_1","unstructured":"Alec Radford. 2018. Improving language understanding by generative pretraining. (2018)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2019.06.053"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2022.3205570"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2023.116916"},{"key":"e_1_3_2_1_32_1","unstructured":"Zhan Tong Yibing Song Jue Wang and Limin Wang. 2022. VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training. arXiv:2203.12602 [cs.CV] https:\/\/arxiv.org\/abs\/2203.12602"},{"key":"e_1_3_2_1_33_1","volume-title":"Attention is all you need. Advances in Neural Information Processing Systems","author":"Vaswani A","year":"2017","unstructured":"A Vaswani. 2017. Attention is all you need. Advances in Neural Information Processing Systems (2017)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2018.01.007"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2019.0949"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2024.3390578"},{"key":"e_1_3_2_1_37_1","volume-title":"Yuzhang Lin, and Hongfu Liu.","author":"Yue Han","year":"2024","unstructured":"Han Yue, Musaab Mohammed Ali, Yuzhang Lin, and Hongfu Liu. 2024. Ultra- Short-Term Forecasting of Large Distributed Solar PV Fleets Using Sparse Smart Inverter Data. IEEE Transactions on Sustainable Energy (2024)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/AAAI.V37I9.26317"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2018.2832634"},{"volume-title":"E3S Web of Conferences","author":"Zhang Xuekai","key":"e_1_3_2_1_41_1","unstructured":"Xuekai Zhang, Yanyong Yang, Huaying Wang, Feitao Zhao, Fangqing Yan, and MengxiaWang. 2020. A Convolutional Neural Network for Regional Photovoltaic Generation Point Forecast. In E3S Web of Conferences, Vol. 185. EDP Sciences, 01079."}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Seoul Republic of Korea","acronym":"CIKM '25"},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761543","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:17:36Z","timestamp":1765502256000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":41,"alternative-id":["10.1145\/3746252.3761543","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761543","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}