用贝叶斯神经网络+蒙特卡洛丢弃法，提升电价预测的不确定性建模能力。
Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting
- 基于贝叶斯神经网络与蒙特卡洛丢弃，捕捉电价预测的不确定性。
- 在逐小时建模下，点预测和区间预测均优于GARCHX与LEAR模型。
- 适合需要风险评估的电力市场决策者使用。
准确的电力价格预测对自由化电力市场的战略决策至关重要，其波动性源于复杂的供需动态和外部因素。传统点预测难以捕捉内在不确定性，限制了其在风险管理中的应用。本文提出一种基于贝叶斯神经网络（BNN）结合蒙特卡洛（MC）丢弃的概率电价预测框架，为每日每小时分别训练独立模型以捕捉昼夜模式。与基准模型——含外生变量的广义自回归条件异方差模型（GARCHX）和LASSO估计的自回归模型（LEAR）——进行关键对比表明，所提模型在点预测和预测区间上均表现更优。该研究为在能源市场预测中应用概率神经模型提供了参考。
原文摘要 · Abstract (English)
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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