Iterative multi-task learning for time-series modeling of solar panel PV outputs
Iterative multi-task learning for time-series modeling of solar panel PV outputs
复制标题
DOI：
10.1016/j.apenergy.2017.12.058
复制
发表时间：
2018-02
期刊：
影响因子：
11.2
通讯作者：
Tahasin Shireen;Chenhui Shao;Hui Wang;Jingjing Li;Xi Zhang;Mingyang Li
中科院分区：
文献类型：
--
作者：
Tahasin Shireen;Chenhui Shao;Hui Wang;Jingjing Li;Xi Zhang;Mingyang Li
太阳能电池板光伏输出的时间序列建模可以帮助太阳能电池板所有者了解电力系统的时变行为，并为负载需求做好准备。由于许多观测数据缺失或缺乏不足以建立统计模型的历史记录，时间序列预测/预测可能会变得具有挑战性。在较长时间段内增加PV测量频率增加了检测PV波动的成本。本文提出了一种有效的方法来迭代多任务学习的时间序列（MTL-GP-TS），提高了预测的光伏输出，而不增加测量工作，通过共享来自多个类似的太阳能电池板的光伏数据之间的信息。所提出的迭代MTL-GP-TS模型学习/估算与感兴趣的太阳能电池板相关的时间序列数据集中的未观测值或缺失值，以预测PV趋势。此外，该方法改进和推广了传统的高斯过程的多任务学习，学习的全局趋势和局部不规则的时间序列的成分。一个真实世界的案例研究表明，所提出的方法可以导致在传统方法的预测大幅改善。本文还讨论了实现该算法时参数和数据源的选择。
Time-series modeling of PV output for solar panels can help solar panel owners understand the power systems’ time-varying behavior and be prepared for the load demand. The time-series forecast/prediction can become challenging due to many missing observations or a lack of historical records that are not sufficient to establish statistical models. Increasing PV measurement frequency over a longer period increases the cost in the detection of the PV fluctuation. This paper proposes an efficient approach to iterative multi-task learning for time series (MTL-GP-TS) that improves prediction of the PV output without increasing measurement efforts by sharing the information among PV data from multiple similar solar panels. The proposed iterative MTL-GP-TS model learns/imputes unobserved or missing values in a dataset of time series associated with the solar panel of interest to predict the PV trend. Additionally, the method improves and generalizes the traditional multi-task learning for Gaussian Process to the learning of both global trend and local irregular components in time series. A real-world case study demonstrated that the proposed method could result in substantial improvement of predictions over conventional approaches. The paper also discusses the selection of parameters and data sources when implementing the proposed algorithm.