Multi-Scale Power Load Forecasting Based on Adaptive GCN and Multi-Head Attention Mechanism. Si

Xufeng Wu; Min Chen; Nan Dong; Yuwen Wu; Zhanzhi Liu; Buyun SU

doi:10.2316/j.2026.203-0657

January 1, 2026International Journal of Power and Energy Systems

Multi-Scale Power Load Forecasting Based on Adaptive GCN and Multi-Head Attention Mechanism. Si

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Key Points

The aim is to enhance the accuracy of power load forecasting by addressing non-stationarity and multi-scale features associated with renewable energy.
Utilized an adaptive graph convolutional network (GCN) for data modeling.
Implemented a multi-head attention mechanism to capture complex relationships in the data.
Conducted various prediction analyses based on renewable energy integration.
Demonstrated increased forecasting accuracy compared to traditional methods.
Achieved improved handling of non-stationary data characteristics.
Highlighted significant gains in multi-scale prediction performance.

Implication

Investigates prediction accuracy of power load in renewable energy contexts, suggesting improved methodologies.

Abstract

With the high proportion of renewable energy connected to the grid,the non-stationarity and multi-scale characteristics of power load arebecoming increasingly prominent, which puts higher demands onprediction accuracy.

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Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/69eefcf4fede9185760d3ab3 https://doi.org/10.2316/j.2026.203-0657

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Multi-Scale Power Load Forecasting Based on Adaptive GCN and Multi-Head Attention Mechanism. Si

Key Points

Implication

Abstract

Discussion

Cite This Study

Multi-Scale Power Load Forecasting Based on Adaptive GCN and Multi-Head Attention Mechanism. Si

Key Points

Implication

Abstract

Discussion

Cite This Study

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