Enhancing Methods for Grid-Load Forecasting in Order to Reduce Grid Losses

Accurate forecasting of electrical load at the substation level is essential for detecting grid losses that pose significant financial and safety challenges. This study investigates whether the standard correction method currently used by grid operators can be improved through the application of advanced forecasting models to granular, location-specific load data. Such improvements are particularly valuable for identifying abnormal consumption patterns indicative of grid losses due to electricity theft, defective meters, or cable damage. This paper evaluates a broad range of statistical and machine learning models—including ARIMAX, SARIMAX, Random Forests, Gradient Boosting Machines, Neural Networks, and Support Vector Regression—based on unique quarter-hourly datasets from several Dutch substations. Two hybrid approaches are proposed, combining the best-performing individual models through a stacked ensemble method and a simpler averaging strategy. The results show that incorporating lagged and additional exogenous variables, along with the application of various advanced models, significantly improves forecasting accuracy compared to the standard correction method, with the best hybrid model reducing MAE and RMSE by approximately 64.7% and 61.6%, respectively, relative to the current operational benchmark. This study demonstrates that substation-level, data-driven forecasting can strengthen the signals used to detect grid losses, offering practical implications for grid operators and policymakers.