A Novel Decomposition-Integration Based Transformer Model for Multi-Scale Electricity Demand Prediction

The accurate forecasting of electricity sales volumes constitutes a critical task for power system planning and operational management. Nevertheless, subject to meteorological perturbations, holiday effects, exogenous economic conditions, and endogenous grid operational metrics, sales data frequently exhibit pronounced volatility, marked nonlinearities, and intricate interdependencies. This inherent complexity compounds modeling challenges and constrains forecasting efficacy when conventional methodologies are applied to such datasets. To address these challenges, this paper proposes a novel decomposition-integration forecasting framework. The methodology first applies Variational Mode Decomposition (VMD) combined with the Zebra Optimization Algorithm (ZOA) to adaptively decompose the original data into multiple Intrinsic Mode Functions (IMFs). These IMF components, each capturing specific frequency characteristics, demonstrate enhanced stationarity and clearer structural patterns compared to the raw sequence, thus providing more representative inputs for subsequent modeling. Subsequently, an improved RevInformer model is employed to separately model and forecast each IMF component, with the final prediction obtained by aggregating all component forecasts. Empirical validation on an annual electricity sales dataset from a commercial building demonstrates the proposed method’s effectiveness and superiority, achieving Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Percentage Error(MSPE）values of 0.044783, 0.211621, and 0.074951 respectively – significantly outperforming benchmark approaches.