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

• URL: http://arxiv.org/abs/2511.11701v1
• Date: Wed, 12 Nov 2025 13:33:28 GMT
• Title: Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting
• Authors: Abhinav Das, Stephan Schlüter,
• Abstract summary: This work presents a framework for probabilistic electricity price forecasting using Bayesian neural networks (BNNs) with Monte Carlo (MC) dropouts.<n>A critical assessment and comparison with the benchmark model, namely: generalized autoregressive conditional heteroskedasticity with a variable (GARCHX) and the LASSO estimated auto-regressive model (LEAR)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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