Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
This work addresses load forecasting for power system operators in ERCOT, but it appears incremental as it applies existing methods to a specific region.
The paper tackled mid-term electricity load forecasting in the ERCOT SCENT region by comparing machine learning models like Linear Regression, XGBoost, LightGBM, and LSTM, and used SHAP for explainability-driven feature engineering to improve accuracy, though no concrete numbers were provided.
Accurate load forecasting is essential to the operation of modern electric power systems. Given the sensitivity of electricity demand to weather variability and temporal dynamics, capturing non-linear patterns is essential for long-term planning. This paper presents a comparative analysis of machine learning models, Linear Regression, XGBoost, LightGBM, and Long Short-Term Memory (LSTM), for forecasting system-wide electricity load up to one year in advance. Midterm forecasting has shown to be crucial for maintenance scheduling, resource allocation, financial forecasting, and market participation. The paper places a focus on the use of a method called "Shapley Additive Explanations" (SHAP) to improve model explainability. SHAP enables the quantification of feature contributions, guiding informed feature engineering and improving both model transparency and forecasting accuracy.