Multi-Source Data Fusion-Based Grid-Level Load Forecasting

This paper introduces a novel weighted fusion methodology for grid-level short-term load forecasting that addresses the critical limitations of direct aggregation methods currently used by regional dispatch centers. Traditional approaches accumulate provincial forecasts without considering regional heterogeneity in load characteristics, data quality, and forecasting capabilities. Our methodology implements a comprehensive evaluation index system that quantifies forecast trustworthiness through three key dimensions: forecast reliability, provincial impact, and forecasting complexity. The core innovation lies in our principal component analysis (PCA)-based weighted aggregation mechanism that dynamically adjusts provincial weights according to their evaluated reliability, further enhancing through time-varying weights that adapt to changing load patterns throughout the day. Experimental validation across three representative seasonal periods (moderate temperature, high temperature, and winter conditions) substantiates that our weighted fusion approach consistently outperforms direct aggregation, achieving a 24.67% improvement in overall MAPE (from 3.09% to 2.33%). Performance gains are particularly significant during critical peak periods, with up to 62.6% error reduction under high-temperature conditions. The methodology verifies remarkable adaptability across different temporal scales, seasonal variations, and regional characteristics, consistently maintaining superior performance from ultra-short-term (1 h) to medium-term (168 h) forecasting horizons. Analysis of provincial weight dynamics reveals intelligent redistribution of weights across seasons, with summer months characterized by Jiangsu dominance (0.30–0.35) shifting to increased Anhui contribution (0.30–0.35) during winter. Our approach provides grid dispatch centers with a computationally efficient solution for enhancing the integration of heterogeneous forecasts from diverse regions, leveraging the complementary strengths of individual provincial systems while supporting safer and more economical power system operations without requiring modifications to existing forecasting infrastructure.