| 基于低秩表示的多任务短期电力负荷预测的研究 |
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| 引用本文： | 苏运,卜凡鹏,郭乃网,田世明,田英杰,张琪祁,瞿海妮,柳劲松. 基于低秩表示的多任务短期电力负荷预测的研究[J]. 现代电力, 2019, 36(3): 58-65 |
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| 作者姓名： | 苏运 卜凡鹏 郭乃网 田世明 田英杰 张琪祁 瞿海妮 柳劲松 |
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| 作者单位： | 1.国家电网上海市电力公司,上海 200122; |
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| 基金项目： | 国网上海市电力公司科技项目(52094016 001Z) |
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| 摘 要： | 在电力系统负荷预测中,使用传统的单任务学习方法未考虑多个地点的负荷间的潜在关系,忽视关联信息在多个地点间传递的可能会导致学习效果欠佳。针对这一问题,本文提出基于低秩表示的多任务学习方法进行多个地点的多任务负荷预测,该方法在学习过程中可以提取不同位置的负荷预测模型的共享低维表示,从而可以挖掘多个任务之间的关联关系,同时又可以区别不同任务之间的差别。实验表明,多任务负荷预测的平均性能优于决策树和随机森林等单任务学习方法,在负荷预测的精度上有了一定的提升。
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| 关 键 词： | 负荷预测 多任务学习 迁移学习 机器学习 低秩 |
| 收稿时间： | 2018-07-11 |
Research on Short-term Load Forecast Using Multi-task With Low-rank Representation |
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| SU Yun,BU Fanpeng,GUO Naiwang,TIAN Shiming,TIAN Yingjie,ZHANG Qigi,QU Haini,LIU Jinsong. Research on Short-term Load Forecast Using Multi-task With Low-rank Representation[J]. Modern Electric Power, 2019, 36(3): 58-65 |
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| Authors: | SU Yun BU Fanpeng GUO Naiwang TIAN Shiming TIAN Yingjie ZHANG Qigi QU Haini LIU Jinsong |
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| Affiliation: | 1.State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China;2.China Electric Power Research Institute, Beijing 100192, China |
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| Abstract: | For load forecasting in power system, latent relationships among different locations are not considered by traditional single task learning method. Neglecting the associated information transfer between multiple locations may lead to poor learning. In order to use latent relationships fully, an approach is proposed to forecast load for multi-locations simultaneously by using multi-task learning with low-rank representation. By extracting shared low dimension representation among load forecasting models at different locations, the approach can mine relationships between multiple tasks, and distinguish differences between different tasks. It is verified by simulation result that the multi-task load forecasting performs on average better than single-task learning algorithms such as decision trees and random forests, and the load forecasting accuracy has been improved. |
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| Keywords: | load forecasting multi-task learning transfer learning machine learning low-rank |
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