Fine-Grained Decomposition of Sector-Specific Grid Load via STL Decomposition and Meteorological Regression

To enhance the interpretability of power grid load forecasting at the sector level and improve the precision of weather attribution, this paper proposes a three-layer analytical framework comprising "daily aggregate STL decomposition – intraday actual proportion reconstruction – sector-specific meteorological regression." Based on 96-point load data from 37 national economic sectors in the Guangzhou power grid during 2021–2025, three consolidation principles are established—parent-child deduplication, homonym normalization, and volume threshold control—to integrate raw entries into 11 decomposition units. Holiday and production schedule effects are first stripped out via pre-regression; hierarchical STL is then employed to extract trend and multi-period seasonal components; and actual intraday load proportions are introduced to correct baseline load reconstruction deviations. Finally, a dual-track Ridge/OLS meteorological regression model is established for individual sectors. The results indicate that the temperature elasticity of the electric power and heat production and supply industry reaches 243.82 MW/℃, representing the primary stress source during system summer peaks; the computer, communication and other electronic equipment manufacturing sector exhibits the strongest model explanatory power (R2 = 0.242); and the transportation sector"s load is dominated by operational schedules, showing extremely low meteorological sensitivity. The proposed method effectively separates baseline load from weather-sensitive load, providing a data foundation for grid dispatching departments to formulate sector-specific and temperature-interval-based forecast correction strategies.