Probabilistic electrical power demand forecasting with uncertainty quantification

2026-09-28

AI Summary

This study addresses the challenge that high penetration of renewable energy exacerbates electricity demand uncertainty, rendering traditional deterministic forecasting inadequate for effective risk quantification. Leveraging real-world power grid data, this work systematically evaluates probabilistic machine learning models, including Natural Gradient Boosting (NGBoost), Bayesian methods, Monte Carlo Dropout, and Gaussian Process Regression. The results demonstrate that NGBoost offers comprehensive advantages in both point forecasting accuracy and uncertainty calibration. It achieves the lowest MAE and RMSE while providing well-calibrated and tight prediction intervals, significantly outperforming baseline models. These findings establish NGBoost as a superior approach, offering more reliable probabilistic decision support for power system planning and operation.

Abstract

The majority of research on electricity consumption forecasting has focused on deterministic approaches, which generate a single point estimate for each time step in the forecasting horizon. However, the increasing penetration of renewable energy sources and the growing complexity of modern smart grids have introduced greater variability and uncertainty into power-system demand and operation. Consequently, probabilistic forecasting, which quantifies the uncertainty and variability associated with future electricity demand, is becoming increasingly important for reliable power-system planning and operation. This study presents an empirical comparison of four contemporary probabilistic forecasting models for electricity consumption, highlighting their respective strengths and limitations. We have performed comparision on real-world power systems related datasets. Across all power-consumption zones, NGBoost demonstrates superior probabilistic forecasting performance, achieving the lowest MAE and RMSE while providing well-calibrated uncertainty estimates with high prediction-interval coverage and reasonably narrow intervals. These results indicate that NGBoost offers a more accurate and reliable forecasting framework than Bayesian, Monte Carlo (MC) Dropout, and Gaussian Process Regression (GPR) models for the considered electricity consumption data.

Problem

• probabilistic forecasting
• electricity demand forecasting
• uncertainty quantification
• smart grids

Innovation

• Probabilistic forecasting
• Uncertainty quantification
• NGBoost
• Electricity demand forecasting
• Smart grids