Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting
统计与深度学习模型在概率电价预测中的不确定性量化分析AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要以电价概率预测与不确定性量化为核心，属预测与即时预测方向；同时涉及 GARCH 等波动率建模，故归入波动率。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
统计与深度学习模型在概率电价预测中的不确定性量化分析
- 研究问题
- 摘要称，研究考察德国电力市场价格的概率预测中，统计模型与深度学习模型如何量化不确定性，尤其是数据不确定性之外的模型与分布选择不确定性。
- 方法与思路
- 作者提出在深度分布神经网络上结合集成、蒙特卡洛 dropout 与保形预测，并将 LASSO 自回归与分位数回归平均、GARCH、保形预测结合，在多种指标下比较。
- 材料陈述的贡献
- 摘要称，基于 LEAR 的模型在概率预测上表现良好且不依赖具体不确定性量化方法；DDNN 同时纳入数据与模型不确定性可改善点预测与概率预测；保形预测对不确定性的刻画最好。
- 阅读关注点
- 应核对德国市场数据区间与划分、各模型设定与超参数、评价指标定义、是否含基准与显著性检验，以及结论对指标选择的敏感性。
- 阅读边界
- 这是 arXiv 预印本，未经同行评审验证；摘要未说明数据样本、指标数值、模型细节与稳健性检验，不能据此判断方法优劣或已获期刊接收。
原文摘要
Precise probabilistic forecasts are fundamental for energy risk management, and there is a wide range of both statistical and machine learning models for this purpose. Inherent to these probabilistic models is some form of uncertainty quantification. However, most models do not capture the full extent of uncertainty, which arises not only from the data itself but also from model and distributional choices. In this study, we examine uncertainty quantification in state-of-the-art statistical and deep learning probabilistic forecasting models for electricity price forecasting in the German market. In particular, we consider deep distributional neural networks (DDNNs) and augment them with an ensemble approach, Monte Carlo (MC) dropout, and conformal prediction to account for model uncertainty. Additionally, we consider the LASSO-estimated autoregressive (LEAR) approach combined with quantile regression averaging (QRA), generalized autoregressive conditional heteroskedasticity (GARCH), and conformal prediction. Across a range of performance metrics, we find that the LEAR-based models perform well in terms of probabilistic forecasting, irrespective of the uncertainty quantification method. Furthermore, we find that DDNNs benefit from incorporating both data and model uncertainty, improving both point and probabilistic forecasting. Uncertainty itself appears to be best captured by the models using conformal prediction. Overall, our extensive study shows that all models under consideration perform competitively. However, their relative performance depends on the choice of metrics for point and probabilistic forecasting.