MULTI-TASK MULTIVARIATE LOAD FORECASTING METHOD INTEGRATING MULTIVARIATE EMPIRICAL MODE DECOMPOSITION AND BAYESIAN OPTIMIZATION

Chen Zikai; Zhang Yong; Song Xianfang; Chen Zhipeng

doi:10.19912/j.0254-0096.tynxb.2024-2138

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 38-45. DOI: 10.19912/j.0254-0096.tynxb.2024-2138

MULTI-TASK MULTIVARIATE LOAD FORECASTING METHOD INTEGRATING MULTIVARIATE EMPIRICAL MODE DECOMPOSITION AND BAYESIAN OPTIMIZATION

Chen Zikai, Zhang Yong, Song Xianfang, Chen Zhipeng

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Abstract

This paper proposes a multi-task multivariate load forecasting method integrating multivariate empirical mode decomposition and Bayesian optimization （MEMD-BO-MMFM）. Firstly, a multi load data recombination method that integrates multi empirical mode decomposition and approximate entropy is proposed, which recombines the decomposition of each load into three categories： random, periodic, and trend components; Subsequently, a Bayesian optimization-based training framework for the gated recurrent unit-multi-task learning （GRU-MTL） model is developed, aiming to identify the optimal network hyperparameters for each category of decomposed components. During the modeling process, the method fully leverages the underlying commonalities across different load types to facilitate the collaborative optimization of multiple load forecasting tasks. The experimental results show that the proposed method outperforms traditional methods in both prediction accuracy and computational efficiency.

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load forecasting / Bayesian method / signal decomposition / integrated energy system / multi-task learning / time series analysis

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Chen Zikai, Zhang Yong, Song Xianfang, Chen Zhipeng. MULTI-TASK MULTIVARIATE LOAD FORECASTING METHOD INTEGRATING MULTIVARIATE EMPIRICAL MODE DECOMPOSITION AND BAYESIAN OPTIMIZATION[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 38-45 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2138

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