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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>\n            Wind is one kind of high-efficient, environmentally-friendly, and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a more and more important role in supplying electricity. Though growing dramatically in recent years, the amount of generated wind power can be directly or latently affected by multiple uncertain factors, such as wind speed, wind direction, temperatures, and so on. More importantly, there exist very complicated dependencies of the generated power on the latent composition of these multiple time-evolving variables, which are always ignored by existing works and thus largely hinder the prediction performances. To this end, we propose\n            <jats:italic>DEWP<\/jats:italic>\n            , a novel\n            <jats:italic>\n              <jats:underline>D<\/jats:underline>\n              eep\n              <jats:underline>E<\/jats:underline>\n              xpansion learning for\n              <jats:underline>W<\/jats:underline>\n              ind\n              <jats:underline>P<\/jats:underline>\n              ower forecasting\n            <\/jats:italic>\n            framework to carefully model the complicated dependencies with adequate expressiveness. DEWP starts with a stack-by-stack architecture, where each stack is composed of (i) a\n            <jats:italic>variable expansion block<\/jats:italic>\n            that makes use of convolutional layers to capture dependencies among multiple variables; (ii) a\n            <jats:italic>time expansion block<\/jats:italic>\n            that applies Fourier series and backcast\/forecast mechanism to learn temporal dependencies in sequential patterns. These two tailored blocks expand raw inputs into different latent feature spaces which can model different levels of dependencies of time-evolving sequential data. Moreover, we propose an\n            <jats:italic>inference block<\/jats:italic>\n            corresponding for each stack, which applies multi-head self-attentions to acquire attentive features and maps expanded latent representations into generated wind power. In addition, to make DEWP more expressive in handling deep neural architectures, we adapt doubly residue learning to process stack-by-stack outputs. Accurate wind power forecasting (WPF) is then better achieved through fine-grained outputs by continuously removing stack residues and accumulating useful stack forecasts. Finally, we present extensive experiments in the real-world WPF application on two datasets from two different turbines, in order to demonstrate the effectiveness of our approach.\n          <\/jats:p>","DOI":"10.1145\/3637552","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T11:42:38Z","timestamp":1702554158000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["DEWP: Deep Expansion Learning for Wind Power Forecasting"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7656-445X","authenticated-orcid":false,"given":"Wei","family":"Fan","sequence":"first","affiliation":[{"name":"University of Oxford, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2725-3334","authenticated-orcid":false,"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"Arizona State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7355-7090","authenticated-orcid":false,"given":"Shun","family":"Zheng","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9472-600X","authenticated-orcid":false,"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2144-1131","authenticated-orcid":false,"given":"Yuanchun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai Shaojie","year":"2018","unstructured":"Shaojie Bai, J. 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