NGBoost improves power demand forecasting with uncertainty estimates
Probabilistic electrical power demand forecasting with uncertainty quantification
Machine LearningArtificial Intelligence
Summary
Electricity use is hard to predict because it varies a lot, especially with more renewable energy sources. Most forecasts only give a single number for future demand, but that misses how uncertain predictions can be. The authors compared four advanced methods that predict a range of possible demands with how confident they are. They found NGBoost to be the best at giving accurate predictions along with good estimates of uncertainty, making it useful for better planning of power systems.
What this means in practice
- •For power system operators: Provide more accurate short-term electricity demand forecasts with explicit uncertainty to improve grid reliability.
- •For energy trading teams: Use better probabilistic demand forecasts to optimize bidding strategies considering forecast uncertainty.
Authors
Mahesh Neupane, Pragya Dhungana, Pradip Khatri, Swechhya Baskota, Hariom Dhungana
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.