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arXiv · 2511.11701

Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting

Abhinav Das·Stephan Schlüter
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Abstract

Accurate electricity price forecasting is critical for strategic decision-making in deregulated electricity markets, where volatility stems from complex supply-demand dynamics and external factors. Traditional point forecasts often fail to capture inherent uncertainties, limiting their utility for risk management. This work presents a framework for probabilistic electricity price forecasting using Bayesian neural networks (BNNs) with Monte Carlo (MC) dropout, training separate models for each hour of the day to capture diurnal patterns. A critical assessment and comparison with the benchmark model, namely: generalized autoregressive conditional heteroskedasticity with exogenous variable (GARCHX) model and the LASSO estimated auto-regressive model (LEAR), highlights that the proposed model outperforms the benchmark models in terms of point prediction and intervals. This work serves as a reference for leveraging probabilistic neural models in energy market predictions.

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Abhinav Das, Stephan Schlüter. 2025-11-12. Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting. https://doi.org/10.1109/eeae65901.2025.11273385

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