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首页> 外文期刊>IEEE transactions on industrial informatics >Two-Layer Transfer-Learning-Based Architecture for Short-Term Load Forecasting
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Two-Layer Transfer-Learning-Based Architecture for Short-Term Load Forecasting

机译：基于两层传输学习的短期负荷预测架构

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摘要

In this paper, by utilizing corresponding load data of source zones, a two-layer transfer-learning-based short-term load forecasting (STLF) architecture is proposed to improve forecasting accuracy of load in target zone. In the inner layer, the latent parameter is introduced to represent the latent factors that results in the differences in electricity consumption behavior between different zones. With the latent parameter as extra input, a latent parameter-assisted model suitable for both the load data of target zone and all source zones is built. Variant weights are assigned to datasets according to their fitness to the latent parameter-assisted model. To solve the weights, an iterative algorithm is developed in the outer layer. Case studies demonstrates that the proposed STLF architecture always improves the forecasting accuracy of classic STLF algorithms, especially when the load data of target zone is insufficient.
机译：本文通过利用源区的相应载荷数据，提出了一种基于双层传输学习的短期负载预测（STLF）架构，以提高目标区域中负载的预测精度。在内层中，引入潜在参数以表示导致不同区域之间的电力消耗行为差异的潜在因子。利用潜在参数作为额外输入，构建适用于目标区域的负载数据和所有源区的潜在参数辅助模型。根据其适合于潜在参数辅助模型将变型权重分配给数据集。为了解决权重，在外层中开发了一种迭代算法。案例研究表明，所提出的STLF架构总是提高经典STLF算法的预测精度，尤其是当目标区域的负载数据不足时。

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