基于TCN和高斯过程残差建模学习的净负荷概率预测方法

赵洪山; 吴雨晨; 潘思潮; 温开云

doi:10.19912/j.0254-0096.tynxb.2023-0632

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (12) : 588-595. DOI: 10.19912/j.0254-0096.tynxb.2023-0632

基于TCN和高斯过程残差建模学习的净负荷概率预测方法

赵洪山, 吴雨晨, 潘思潮, 温开云

作者信息 +

PROBABILISTIC NET LOAD FORECASTING BASED ON TCN AND GAUSSIAN PROCESS-ENABLED RESIDUAL MODELING LEARNING APPROACH

Zhao Hongshan, Wu Yuchen, Pan Sichao, Wen Kaiyun

Author information +

文章历史 +

摘要

提出一种基于时间卷积神经网络（TCN）和高斯过程（GP）的净负荷预测方法,可提供精确的点预测和概率预测结果。首先,TCN被用来提取大量的历史数据中净负荷的变化规律,TCN优秀的时间序列建模能力可发现净负荷预测任务输入输出之间的复杂映射关系。然后,为高斯过程设计一个复合核函数对TCN的预测残差进行建模学习,该过程可在TCN预测的基础上进一步提升点预测的精度,同时也可利用高斯过程的不确定性量化能力对净负荷预测的不确定性进行量化。最后,通过在真实净负荷数据集上和大量先进的模型进行比较,验证该文提出方法的有效性。

Abstract

Net load forecasting is of great importance for grid operation and management with high penetration of renewable energy. This paper proposes a net load prediction method based on temporal convolutional network （TCN） and Gaussian process （GP）, which can provide accurate point prediction and probabilistic prediction results. First, TCN is used to extract the variation pattern of net load from a large amount of historical data, and the excellent time series modeling capability of TCN can discover the complex mapping relationship between the input and output of the net load forecasting task. Then, a composite kernel function is designed for the Gaussian process to correct the prediction residuals of TCN, which can further improve the accuracy of point prediction based on TCN prediction, and also quantify the uncertainty of net load prediction by using the uncertainty quantification ability of Gaussian process. Finally, experiments are conducted on real net load datasets, and the effectiveness of the proposed method is illustrated by comparing it with a large number of state-of-the-art models.

导出引用

赵洪山, 吴雨晨, 潘思潮, 温开云. 基于TCN和高斯过程残差建模学习的净负荷概率预测方法[J]. 太阳能学报. 2024, 45(12): 588-595 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0632

Zhao Hongshan, Wu Yuchen, Pan Sichao, Wen Kaiyun. PROBABILISTIC NET LOAD FORECASTING BASED ON TCN AND GAUSSIAN PROCESS-ENABLED RESIDUAL MODELING LEARNING APPROACH[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 588-595 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0632

中图分类号： TM743

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