生成式概率时间序列预测及其在电网运行中的应用
Generative Probabilistic Time Series Forecasting and Applications in Grid Operations
AI总结：
本文提出弱新息自编码器架构，从非参数平稳时间序列中提取独立同分布新息序列，并证明其贝叶斯充分性，在实时电价预测中优于领先的概率预测和点预测方法。
摘要：
生成式概率预测根据给定过去时间序列观测的条件概率分布生成未来时间序列样本。此类技术在基于风险的不确定性决策与规划中至关重要，并在电网运行中具有广泛应用，包括电价预测、基于风险的经济调度和随机优化。受 Wiener 和 Kallianpur 的新息表示启发，我们提出了一种弱新息自编码器架构及一种学习算法，用于从非参数平稳时间序列中提取独立同分布的新息序列。我们证明弱新息序列具有贝叶斯充分性，这使得所提出的弱新息自编码器成为生成式概率预测的规范架构。所提出的技术被应用于预测高度波动的实时电价，在多个预测指标上展现出优于领先的概率预测和点预测技术的性能。
英文摘要：
Generative probabilistic forecasting produces future time series samples according to the conditional probability distribution given past time series observations. Such techniques are essential in risk-based decision-making and planning under uncertainty with broad applications in grid operations, including electricity price forecasting, risk-based economic dispatch, and stochastic optimizations. Inspired by Wiener and Kallianpur's innovation representation, we propose a weak innovation autoencoder architecture and a learning algorithm to extract independent and identically distributed innovation sequences from nonparametric stationary time series. We show that the weak innovation sequence is Bayesian sufficient, which makes the proposed weak innovation autoencoder a canonical architecture for generative probabilistic forecasting. The proposed technique is applied to forecasting highly volatile real-time electricity prices, demonstrating superior performance across multiple forecasting measures over leading probabilistic and point forecasting techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
