Climate-Driven Load Variations and Fault Risks in Humid-Subtropical Mountainous Grids: A Hybrid Forecasting and Resilience Framework

Against the backdrop of global climate change, remote subtropical mountainous power grids face severe operational challenges due to their fragile infrastructure and complex climatic conditions. However, existing research has insufficiently addressed load forecasting in data-sparse regions, particularly lacking systematic analysis of the “meteorology–load–failure” coupling mechanism. To address this gap, this study focused on 10 kV distribution lines in a typical subtropical monsoon region of southern China. Based on hourly load and meteorological data from 2016 to 2025, we propose a two-stage hybrid model combining “Random Forest (RF) feature selection + Long Short-Term Memory (LSTM) time series forecasting”. Through deep feature engineering, composite, lagged, and interactive features were constructed. Using the RF algorithm, we quantitatively identified the core drivers of load variation across different time scales: at the hourly scale, variations are dominated by historical inertia (with weights of 0.5915 and 0.3757 for 1-h and 24-h lagged loads, respectively); at the daily scale, the logic shifts to meteorological triggering and cumulative effects, where the composite feature load_lag1_hi_product emerged as the most critical driver (weight of 0.8044). Experimental results demonstrate that the hybrid model significantly improved forecasting accuracy compared to the full-feature LSTM benchmark: on a daily scale, RMSE decreased by 13.29% and MAE by 16.67%, with R2 reaching 0.8654; on an hourly scale, R2 reached 0.9687. Furthermore, correlation analysis with failure data revealed that most grid faults occurred during intervals of extremely low load variation (0–5%), suggesting that “chronic stress” from environmental exposure in hot and humid conditions is the primary cause, with lightning identified as the leading external threat (26.90%). The interpretable forecasting framework proposed in this study transcends regional limitations. It provides a strategic “low-cost, high-resilience” prototype applicable to power systems in humid-subtropical zones worldwide, particularly for developing regions facing the dual challenges of data sparsity and climate vulnerability.