Multistep photovoltaic power forecasting based on multi-timescale fluctuation aggregation attention mechanism and contrastive learning

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems Pub Date : 2024-12-01 DOI:10.1016/j.ijepes.2024.110389

Liang Yuan , Xiangting Wang , Yao Sun , Xubin Liu , Zhao Yang Dong

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引用次数: 0

Abstract

The integration of photovoltaic (PV) power into electrical grids introduces significant uncertainty due to the inherent volatility and intermittency of solar energy, underscoring the need for precise short and medium-term PV power forecasting. Despite the superior performance of Transformer-based time series methods, their application to PV power prediction remains suboptimal. In response to this deficiency, this paper proposes a novel attention mechanism that aggregates fluctuations across multiple time scales. This mechanism enhances the segmentation and extraction of nonlinear correlations between PV power outputs and meteorological factors, assigning variable weights to patterns of change across different time scales. Furthermore, a novel approach for selecting similar days is also developed based on contrastive learning, which enables self-supervised identification of similarities among PV power samples and enhances the model’s attention to local dynamic variations. Comparative analysis with eight state-of-the-art benchmark methods shows that the proposed MFA-attention model achieves lower prediction errors and improved effectiveness.

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基于多时间尺度波动聚集注意机制和对比学习的光伏电力多步预测

由于太阳能固有的不稳定性和间歇性，将光伏发电并入电网带来了很大的不确定性，强调需要精确的短期和中期光伏发电预测。尽管基于变压器的时间序列方法具有优越的性能，但它们在光伏发电功率预测中的应用仍然不够理想。针对这一不足，本文提出了一种新的注意力机制，该机制在多个时间尺度上聚合波动。该机制增强了光伏发电输出与气象因子之间非线性相关性的分割和提取，为不同时间尺度的变化模式分配了可变权重。此外，本文还提出了一种基于对比学习的相似日选择方法，该方法能够自监督地识别光伏发电样本之间的相似性，增强了模型对局部动态变化的关注。与8种最先进的基准方法的对比分析表明，本文提出的mfa -注意力模型具有较低的预测误差和较好的预测效果。©2017 Elsevier Inc.版权所有。

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来源期刊

International Journal of Electrical Power & Energy Systems 工程技术-工程：电子与电气

CiteScore

12.10

自引率

17.30%

发文量

1022

审稿时长

51 days

期刊介绍： The journal covers theoretical developments in electrical power and energy systems and their applications. The coverage embraces: generation and network planning; reliability; long and short term operation; expert systems; neural networks; object oriented systems; system control centres; database and information systems; stock and parameter estimation; system security and adequacy; network theory, modelling and computation; small and large system dynamics; dynamic model identification; on-line control including load and switching control; protection; distribution systems; energy economics; impact of non-conventional systems; and man-machine interfaces. As well as original research papers, the journal publishes short contributions, book reviews and conference reports. All papers are peer-reviewed by at least two referees.