MSTL-NNAR: a new hybrid model of machine learning and time series decomposition for wind speed forecasting
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00477-024-02701-7.pdf
Reference47 articles.
1. Ai X, Li S, Xu H (2023) Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory. Front Energy Res 10:1043867
2. Al Dhaheri K, Woon WL, Aung Z (2017) Wind speed forecasting using statistical and machine learning methods: a case study in the UAE. In: Data analytics for renewable energy integration: informing the generation and distribution of renewable energy: 5th ECML PKDD workshop, DARE 2017, Skopje, Macedonia, September 22, 2017, Revised Selected Papers 5. Springer, pp 107–120
3. Alhussan AA, El-Kenawy E-SM, Abdelhamid AA, Ibrahim A, Eid MM, Khafaga DS (2023) Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms. Front Energy Res 11:1172176
4. Ammar E, Xydis G (2024) Wind speed forecasting using deep learning and preprocessing techniques. Int J Green Energy 21(5):988–1016
5. Bandara K, Hyndman RJ, Bergmeir C (2021) MSTL: a seasonal-trend decomposition algorithm for time series with multiple seasonal patterns. arXiv preprint arXiv:2107.13462
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3