Two-stage forecasting of TCN-GRU short-term load considering error compensation and real-time decomposition

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yang Li, Yongsheng Ye, Yanlong Xu, Lili Li, Xi Chen, Jianghua Huang
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Abstract

With the continuous development of power system and the growth of load demand, accurate short-term load forecasting (SLTF) provides reliable guidance for power system operation and scheduling. Therefore, this paper proposes a two-stage short-term load forecasting method. In the first stage, the original load is processed by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The time series features of the load are extracted by temporal convolutional network (TCN), which is used as an input to realize the initial load prediction based on gated recurrent unit (GRU). At the same time, in order to overcome the problem that the prediction model established by the original subsequence has insufficient adaptability in the newly decomposed subsequence, the real-time decomposition strategy is adopted to improve the generalization ability of the model. To further improve the prediction accuracy, an error compensation strategy is constructed in the second stage. The strategy uses adaptive variational mode decomposition (AVMD) to reduce the unpredictability of the error sequence and corrects the initial prediction results based on the temporal convolutional network-gated recurrent unit (TCN-GRU) error compensator. The proposed two-stage forecasting method was evaluated using load data from Queensland, Australia. The analysis results show that the proposed method can better capture the nonlinearity and non-stationarity in the load data. The mean absolute percentage error of its prediction is 0.819%, which are lower than the other compared models, indicating its high applicability in SLTF.

Abstract Image

考虑误差补偿和实时分解的 TCN-GRU 短期负荷两阶段预测
随着电力系统的不断发展和负荷需求的增长,准确的短期负荷预测(SLTF)为电力系统的运行和调度提供了可靠的指导。因此,本文提出了一种两阶段短期负荷预测方法。在第一阶段,用带自适应噪声的改进型完全集合经验模式分解(ICEEMDAN)处理原始负荷。通过时序卷积网络(TCN)提取负荷的时间序列特征,并以此为输入,实现基于门控递归单元(GRU)的初始负荷预测。同时,为了克服原始子序列建立的预测模型在新分解的子序列中适应性不足的问题,采用了实时分解策略来提高模型的泛化能力。为了进一步提高预测精度,第二阶段构建了误差补偿策略。该策略使用自适应变异模式分解(AVMD)来降低误差序列的不可预测性,并基于时序卷积网络门控递归单元(TCN-GRU)误差补偿器修正初始预测结果。利用澳大利亚昆士兰州的负荷数据对所提出的两阶段预测方法进行了评估。分析结果表明,所提出的方法能更好地捕捉到负荷数据中的非线性和非平稳性。其预测的平均绝对百分比误差为 0.819%,低于其他比较模型,表明其在 SLTF 中具有较高的适用性。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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