A Physics‐Constrained Adaptive Coupling Framework for Plateau Highway Load Forecasting

ABSTRACT

Short‐term load forecasting serves as a critical prerequisite for microgrid energy management to facilitate the efficient in situ absorption of the abundant photovoltaic resources along plateau corridors by local highway loads. It faces severe challenges posed by strong nonstationarity, nonlinearity and spatiotemporal heterogeneity, which are induced by traffic pulses driven by natural climate and tourism. To address these challenges, this paper proposes a physics‐constrained hybrid forecasting framework that integrates CEEMDAN decomposition, sample entropy‐altitude weighted (SE‐AW) K‐means clustering, altitude‐adaptive (AA) kernel principal component analysis and Transformer‐based prediction. First, a novel SE‐AW K‐means is developed to reconstruct the dataset. By introducing SE to quantify load burstiness and embedding altitude constraints, this method overcomes the limitations of standard K‐means, effectively decoupling nodes driven by random meteorological shocks from those with regular traffic patterns. Second, based on the clustered subdatasets, an AA‐KPCA module is employed to extract nonlinear features, followed by a physics‐constrained Transformer to capture long‐range temporal dependencies. Experiments using actual operational data show that the proposed method outperforms the benchmark model and maintains good predictive performance under different altitude and seasonal conditions. In addition, qualitative curve tracking analysis and quantitative error evaluation were conducted on the prediction results to verify whether the prediction deviation remained within an acceptable engineering range. These results provide technical support for the scheduling of microgrids on high‐altitude highways and the consumption of local renewable energy.