针对单一深度学习模型难以兼顾长短期依赖与局部特征波动的问题,提出一种基于经验模态分解（EMD）、Transformer和时间卷积网络（TCN）的双通道耦合预测模型。模型利用EMD将原始光伏序列分解为一组本征模态函数（IMF）分量,针对不同平稳性的IMF分量,分别采用基于残差网络改进的Transformer模型（Enresformer）和基于门控网络改进的TCN模型进行预测,并集成预测结果。引入误差修正机制以进一步提升预测精度。基于多个实际光伏数据集的对比实验表明,所提模型在预测精度方面显著优于当前主流的深度学习模型和耦合模型,为功率预测提供了一种高效且可靠的解决方案。
Abstract
Photovoltaic （PV） power generation, as a crucial source of clean energy, requires accurate power forecasting to facilitate efficient power dispatch. PV power is highly influenced by meteorological factors and exhibits significant intermittency and volatility. Traditional time series forecasting models often fail to capture the dynamic correlations among meteorological factors, leading to limited forecasting accuracy. Meanwhile, single deep learning architectures struggle to effectively capture the intricate interplay between multi-scale temporal dependencies and localized feature variations. To address these challenges, this paper proposes a dual-channel hybrid forecasting model integrating empirical mode decomposition （EMD）, Transformer, and temporal convolutional network （TCN）. EMD is employed to decompose the raw PV power signal into a set of intrinsic mode functions （IMFs）. The stationarity of each IMF component is quantified using sample entropy （SE）, guiding the selection of tailored predictive models for components with distinct stationarity characteristics. An enhanced residual transformer （Enresformer）, which incorporates residual networks, and a gated network-enhanced TCN model are employed to forecast these components, with subsequent ensembling of their outputs. An error correction mechanism is introduced to further refine prediction accuracy. Comparative experiments based on multiple real-world PV datasets show that the proposed model significantly outperforms existing deep learning and hybrid models in terms of forecasting accuracy, providing an efficient and reliable solution for PV power prediction in practical power stations.
关键词
光伏功率 /
预测 /
经验模态分解 /
Transformer /
残差网络 /
时间卷积网络
Key words
photovoltaic power /
forecasting /
empirical mode decomposition /
transformer /
residual network /
temporal convolutional network
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基金
国家重点研发计划（2024YFHZ0318）
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