TY  - EJOU
AU  - Xiao, Tianxiong 
AU  - Zou, Zhi 
AU  - Yan, Hai 
AU  - Fu, Linjing 
AU  - Zhu, Yifei 
AU  - Zhang, Lei 
AU  - Chen, Xi 

TI  - A Real-Time Forecasting Framework for Non-Stationary Three-Phase Loads with Inter-Phase Dependency
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - Due to the complex operating characteristics of loads in power distribution networks, three-phase loads exhibit strong non-stationarity and complex phase-to-phase coupling, thereby adversely affecting the power quality of distribution networks. However, existing decomposition-based forecasting methods usually rely on global or offline feature extraction and insufficiently consider local frequency drift, inter-phase dependency, and real-time deployment. To address these limitations, this paper proposes a causality-preserving real-time forecasting framework that integrates snow ablation optimization-assisted short-time variational mode decomposition (SAO-STVMD) with a serial gated hybrid neural network. Specifically, SAO is used to adaptively determine the key STVMD parameters, and an overlap-add rolling reconstruction strategy is introduced to suppress boundary discontinuities and reduce redundant window-wise computation. A trend–residual serial decoupling architecture is then designed to separately model slow-varying load evolution and fast-varying disturbances, while cross-phase attention (CPA) is employed to capture inter-phase dependency. In addition, a gated fusion unit is introduced as a dynamic risk trade-off mechanism rather than a pure average-error minimization module. Comparative and ablation results demonstrate the effectiveness of the proposed framework in improving forecasting accuracy under an online-compatible and causality-preserving SAO-STVMD feature-generation protocol. Additional causality audits, including prefix recomputation and future masking tests, confirm that the IMF features used at each forecasting time step are generated only from available historical observations, thereby ruling out implicit look-ahead bias in the decomposition stage. Further component-attribution analysis indicates that the dominant reduction in average forecasting error is mainly attributed to the localized STVMD decomposition stage. Under STVMD-decomposed inputs, CPA-SHNet provides additional gains by enhancing inter-phase dependency modeling. Beyond average-error performance, the gated fusion unit further serves as a conditional tail-risk mitigation mechanism, improving robustness for most high-error transient events produced by fixed additive reconstruction.
KW  - Three-phase load forecasting; short-time variational mode decomposition (STVMD); snow ablation optimizer (SAO); cross-phase attention (CPA); dynamic risk trade-off

DO  - 10.32604/ee.2026.083233