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

TL;DR

This paper compares machine learning models for mid-term electricity load forecasting in ERCOT's SCENT region, emphasizing explainability through SHAP to enhance feature engineering, transparency, and accuracy.

Contribution

It introduces an explainability-driven feature engineering approach using SHAP for improved load forecasting in power systems.

Findings

XGBoost and LightGBM outperform Linear Regression and LSTM in accuracy.

SHAP-based feature engineering enhances model interpretability and performance.

Explainability methods guide effective feature selection for electricity load prediction.

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.