Bayesian Modeling and Forecasting of Intraday Electricity Load
日内电力负荷的贝叶斯建模与预测AI 解读 ↓
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日内电力负荷的贝叶斯建模与预测
- 研究问题
- 如何同时预测日内各时段电力负荷及每日峰值的时点和水平，并反映天气、季节与时段间相关结构带来的不确定性？
- 方法与思路
- 作者建立多方程回归与对角一阶平稳VAR模型，用贝叶斯模型选择考察回归项及扰动相关结构，并以MCMC获得预测分布。
- 材料陈述的贡献
- 摘要称方法提供负荷及峰值时点和水平的分布推断；澳大利亚半小时负荷应用中，预测均值与同模型的迭代GLS预测相比表现有利。
- 阅读关注点
- 应核对先验与模型选择设定、预测评价的样本划分，以及温度预测信息的可得性和峰值预测分布的校准表现。
- 阅读边界
- 仅据版本未注明的OpenAlex摘要，无法核实预测改进幅度、评价设计或计算诊断；结果依赖所述模型、地区与天气预测信息。
原文摘要
The advent of wholesale electricity markets has brought renewed focus on intraday electricity load forecasting. This article proposes a multi-equation regression model with a diagonal first-order stationary vector autoregresson (VAR) for modeling and forecasting intraday electricity load. The correlation structure of the disturbances to the VAR and the appropriate subset of regressors are explored using Bayesian model selection methodology. The full spectrum of finite-sample inference is obtained using a Bayesian Markov chain Monte Carlo sampling scheme. This includes the predictive distribution of load and the distribution of the time and level of daily peak load, something that is difficult to obtain with other methods of inference. The method is applied to several multiequation models of half-hourly total system load in New South Wales, Australia. A detailed model based on 3 years of data reveals trend, seasonal, bivariate temperature/humidity, and serial correlation components that all vary intraday, justifying the assumption of a multiequation approach. Short-term forecasts from simple models highlight the gains that can be made if accurate temperature predictions are exploited. Bayesian predictive means for half-hourly load compare favorably with point forecasts obtained using iterated generalized least squares estimation of the same models.