LCDFormer: Long-term correlations dual-graph transformer for traffic forecasting

Cai, Jiongbiao; Wang, Chia-Hung; Hu, Kun

doi:10.1016/j.eswa.2024.123721

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…The results in Tables 4 and 5 reveal a similarity in the experimental outcomes between the MVMD-based models. In order to establish the statistical significance of the results displayed in Tables 4 and 5, we introduce the Friedman and post-hoc Nemenyi tests [30] to assess the differences among various models for evaluation. The null hypothesis of the Friedman test is that there is no difference among all comparison methods in the 24 datasets of Case A and Case B.…”

Section: Model Descriptionmentioning

confidence: 99%

Short-Term Load Forecasting for Residential Buildings Based on Multivariate Variational Mode Decomposition and Temporal Fusion Transformer

Ye,

Zhu,

Zhang

2024

Energies

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Short-term load forecasting plays a crucial role in managing the energy consumption of buildings in cities. Accurate forecasting enables residents to reduce energy waste and facilitates timely decision-making for power companies’ energy management. In this paper, we propose a novel hybrid forecasting model designed to predict load series in multiple households. Our proposed method integrates multivariate variational mode decomposition (MVMD), the whale optimization algorithm (WOA), and a temporal fusion transformer (TFT) to perform one-step forecasts. MVMD is utilized to decompose the load series into intrinsic mode functions (IMFs), extracting characteristics at distinct scales. We use sample entropy to determine the appropriate number of decomposition levels and the penalty factor of MVMD. The WOA is utilized to optimize the hyperparameters of MVMD-TFT to enhance its overall performance. We generate two distinct cases originating from BCHydro. Experimental results show that our method has achieved excellent performance in both cases.

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Section: Model Descriptionmentioning

confidence: 99%