...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Renewable energy >Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models
【24h】

Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models

机译：概率风力预测使用有限混合高斯工艺回归模型的选择性集合

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Ensemble learning models have been widely used for wind power forecasting to facilitate efficient dispatching of power systems. However, traditional ensemble methods cannot always function well due to insufficient accuracy and diversity of base learners, ignorance of ensemble pruning, as well as the lack of adaptation capability. Therefore, a novel probabilistic wind power forecasting method is proposed based on selective ensemble of finite mixture Gaussian process regression models (SEFMGPR). First, a set of diverse local Gaussian process regression (GPR) models are constructed through multimodal perturbation mechanism, i.e., perturbing the training data and input attributes simultaneously. Then, a set of finite mixture GPR models (FMGPR) is built by integrating local GPR models through finite mixture mechanism (FMM). Next, the highly influential FMGPR models are selected using genetic algorithm (GA) based ensemble pruning. When a new test sample comes, the component predictions from the selected FMGPR models are adaptively combined by using FMM again and the probabilistic prediction results of the SEFMGPR model are obtained. Besides, an incremental adaptation mechanism is used to alleviate performance degradation of SEFMGPR. The application results from a real wind farm dataset show that SEFMGPR outperforms the traditional global and ensemble wind power prediction methods, and can maintain high prediction accuracy by effectively handling time-varying changes of wind power data.(c) 2021 Elsevier Ltd. All rights reserved.
机译：集合学习模型已广泛用于风力预测，以便于高效调度电力系统。然而，由于基础学习者的准确性和多样性，对基本学习者的无知，集成修剪无知，以及缺乏适应能力，传统的集合方法不能始终运作。因此，提出了一种基于有限混合物高斯工艺回归模型（SEFMGPR）的选择性集合的新型概率风力预测方法。首先，通过多模式扰动机制，即同时扰乱训练数据和输入属性，构建了一组不同的本地高斯进程回归（GPR）模型。然后，通过通过有限的混合物机制（FMM）集成局部GPR模型，构建一组有限混合物GPR模型（FMGPR）。接下来，使用基于基于遗传算法（GA）的集合修剪来选择高度影响力的FMGPR模型。当新的测试样本来实现时，通过使用FMM再次自适应地组合来自所选择的FMGPR模型的组件预测，并且获得了SEFMGPR模型的概率预测结果。此外，增量适应机制用于缓解SEFMGPR的性能下降。真正的风电场数据集的应用结果表明，SEFMGPR优于传统的全球和集合风电预测方法，并通过有效处理风电数据的时变变化来保持高预测精度。（c）2021 Elsevier有限公司预订的。

著录项

  • 来源
    《Renewable energy》 |2021年第8期|1-18|共18页
  • 作者

    Jin Huaiping; Shi Lixian; Chen Xiangguang; Qian Bin; Yang Biao; Jin Huaikang;

  • 作者单位

    Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China|Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China;

    Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China|Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China;

    Beijing Inst Technol Sch Chem & Chem Engn Beijing 100081 Peoples R China;

    Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China|Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China;

    Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China|Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China;

    Huaneng Renewables Co Ltd Yunnan Branch Kunming 650000 Yunnan Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Wind power forecasting; Ensemble learning; Gaussian process regression; Probabilistic modeling; Ensemble pruning; Model adaptation;

    机译：风力预测;集合学习;高斯过程回归;概率造型;集合修剪;模型适应;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号