[1]WANG Dewen,AN Han,ZHANG Linfei,et al.Multi-task load forecasting of integrated energy based on progressive layered feature extraction[J].CAAI Transactions on Intelligent Systems,2025,20(4):858-870.[doi:10.11992/tis.202406008]
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Multi-task load forecasting of integrated energy based on progressive layered feature extraction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 858-870 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
Multi-task load forecasting of integrated energy based on progressive layered feature extraction
Author(s):
WANG Dewen1; 2;  AN Han1;  ZHANG Linfei1;  ZHAO Wenqing1; 3
1. Department of Computer, North China Electric Power University, Baoding 071003, China;
2. Hebei Key Laboratory of Knowledge Computing for Energy & Power, North China Electric Power University, Baoding 071003, China;
3. Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, North China Electric Power University, Baoding 071003, China
Keywords:
load forecasting; integrated energy; multi-task learning; multiple loads; progressive layered; feature extraction; maximum information coefficient; variational mode decomposition
CLC:
TP18
DOI:
10.11992/tis.202406008
Abstract:
Due to the complex coupling relationship between electric, cold and heat loads in an integrated energy system, it is difficult for traditional multi-task learning models to learn effective multi-load coupling characteristics, which may lead to reduced prediction accuracy. In this paper, a comprehensive energy multi-task load forecasting model with progressive layered feature extraction is proposed, considering the complex coupling relationship of multiple loads. Firstly, divide the annual data by season and analyze the coupling strength between electricity, cooling, and heating loads in each season. Then, by using variational mode decomposition, the historical load sequence is decomposed into multiple components of different frequencies, which can better explore the deep time series features of multiple loads. Finally, the coupling features of multiple loads are extracted progressively and the influence weights of the coupling features on the prediction results are dynamically allocated to avoid the degradation of the model prediction accuracy when the coupling features are invalid. Experimental results show that the proposed model has better performance in terms of prediction accuracy under different multi-component load coupling intensities. The conclusion can be used to guide the process of load forecasting of integrated energy.
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