Quantifying Uncertainty in Energy Demand Forecasts: A Probabilistic Modeling Approach

This paper presents an innovative approach to energy demand forecasting through the implementation of a probabilistic deep learning framework that explicitly quantifies prediction uncertainties. We develop a Gated Recurrent Unit (GRU) neural network architecture enhanced with Monte Carlo dropout for probabilistic predictions. The model demonstrates robust performance in forecasting hourly energy consumption patterns while providing reliable uncertainty estimates. Our results show significant improvements in prediction accuracy, achieving an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.8547 and a mean absolute error of 0.0412, while successfully capturing both aleatory and epistemic uncertainties in the predictions. The proposed framework offers valuable insights for grid operators and energy policymakers, contributing to more reliable and efficient energy management systems.