Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region

• URL: http://arxiv.org/abs/2507.22220v1
• Date: Tue, 29 Jul 2025 20:36:24 GMT
• Title: Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
• Authors: Abhiram Bhupatiraju, Sung Bum Ahn,
• Abstract summary: This paper presents a comparative analysis of machine learning models, for forecasting system-wide electricity load up to one year in advance.<n>The paper places a focus on the use of a method called "Shapley Additive Explanations" (SHAP) to improve model explainability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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