Research: Probabilistic Forecasting of Short-Term Energy Demand with Quantified Predict...

Research question: How can probabilistic forecasting methods with uncertainty quantification improve the accuracy and reliability of short-term energy demand predictions compared to traditional point forecasting models?

Background & Motivation: Accurate short-term energy demand forecasting is essential for grid stability, operational planning, and integration of renewable resources in modern power systems. Traditional point forecasts provide a single estimate, neglecting the inherent uncertainty in demand, which may lead to suboptimal or risky decisions for system operators.

Research Gap / Question: Recent advances in probabilistic forecasting and deep learning provide opportunities to model not only the expected demand but also the uncertainty around it. However, the comparative effectiveness of these techniques, alongside rigorous uncertainty quantification, remains underexplored for high-resolution energy demand datasets.

Approach & Expected Contribution: This study will critically review existing literature, empirically evaluate state-of-the-art probabilistic forecasting models (e.g., quantile regression, Bayesian neural networks) on public energy demand datasets, and assess their uncertainty quantification capabilities against traditional methods. The research will design experiments to compare model calibration, sharpness, and operational utility.

Why it matters: Providing reliable probabilistic forecasts with quantified uncertainty enables grid operators and energy providers to make more informed, risk-aware decisions, thereby improving reliability and efficiency in energy management.