| 引用本文: | 廖志伟,陈琳韬,黄杰栋,张文锦.基于多智能集成学习的中短期电煤价格预测[J].控制理论与应用,2021,38(12):1968~1978.[点击复制] |
| LIAO Zhi-wei,CHEN Lin-tao,HUANG Jie-dong,ZHANG Wen-jing.Medium and short-term electricity coal price prediction based on multi-intelligence ensemble learning[J].Control Theory and Technology,2021,38(12):1968~1978.[点击复制] |
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| 基于多智能集成学习的中短期电煤价格预测 |
| Medium and short-term electricity coal price prediction based on multi-intelligence ensemble learning |
| 摘要点击 1304 全文点击 469 投稿时间：2020-08-12 修订日期：2021-02-06 |
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| DOI编号 10.7641/CTA.2021.00529 |
| 2021,38(12):1968-1978 |
| 中文关键词 集成学习 电煤价格 滚动预测 灵敏性差异 XGBoost 长短神经网络 |
| 英文关键词 ensemble learning electricity coal price rolling prediction sensitivity difference XGBoost long-short term memory |
| 基金项目 国家自然科学基金项目(51437006)资助. |
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| 中文摘要 |
| 为扩大电力市场交易量与下调市场电价, 需要提升电煤价格预测的可靠性与准确性. 为此本文提出了多智
能集成学习的中短期电煤价格预测方法. 首先, 阐述了Stacking集成学习的结构和原理; 然后, 介绍了数种智能电煤
价格的预测模型, 并通过算例证明了不同单智能模型对数据的感知能力存在差异性; 进而, 通过比较单智能模型预
测结果的差异值均差, 筛选出预测性能优异并且数据感知角度差异性明显的智能模型组. 为了充分发挥个模型感
知能力差异性的优势, 利用Stacking融合各模型, 得到一种适用于电煤价格滚动预测的集成模型. 最后, 通过滚动预
测2019至2020年的电煤价格, 对集成模型的有效性进行验证. |
| 英文摘要 |
| In order to expand the trading volume of the electricity market and lower the electricity price, it is necessary to
improve the reliability and accuracy of electricity coal price prediction. Therefore, a multi-intelligence ensemble learning
method for medium and short-term electricity coal price prediction is proposed in this paper. First, the structure and
principle of Stacking ensemble learning model are explained. Then, several intelligent coal price prediction models are
introduced, and the difference of data perception ability of different single intelligent models is proved through calculation
examples. Furthermore, by comparing the average difference of the prediction results of a single intelligent model, the
intelligent model group with excellent prediction performance and obvious difference in data perception is selected. To
give full play to the advantages of the differences in the perception ability of each model, the Stacking is used to fuse the
models to obtain an ensemble model suitable for rolling prediction of electricity coal prices. Finally, the effectiveness of
the ensemble model is verified by rolling prediction of the electricity coal price from 2019 to 2020. |
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