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首页> 外文期刊>Energy economics >On the importance of the long-term seasonal component in day-ahead electricity price forecasting Part Ⅱ - Probabilistic forecasting
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On the importance of the long-term seasonal component in day-ahead electricity price forecasting Part Ⅱ - Probabilistic forecasting

机译：关于长期季节性成分在最新电力价格预测中的重要性Ⅱ - 概率预测

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A recent electricity price forecasting study has shown that the Seasonal Component AutoRegressive (SCAR) modeling framework, which consists of decomposing a series of spot prices into a trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same autoregressive model is calibrated to the prices themselves. Here, we show that further accuracy gains can be achieved when the explanatory variables (load forecasts) are deseasonalized as well. More importantly, considering a novel extension of the SCAR concept to probabilistic forecasting and applying two methods of combining predictive distributions, we find that (i) SCAR-type models nearly always significantly outperform the autoregressive benchmark but are in turn outperformed by combined SCAR forecasts, (ii) predictive distributions computed using Quantile Regression Averaging (QRA) outperform those obtained from historical simulation and bootstrap methods, and (iii) averaging over predictive distributions generally yields better probabilistic forecasts of electricity spot prices than averaging over quantiles. Given that probabilistic forecasting is a concept closely related to risk management, our study has important implications for risk officers and portfolio managers in the power sector. (C) 2018 Elsevier B.V. All rights reserved.
机译：最近的一项电力价格预测研究表明，季节性组件自回归（瘢痕）建模框架，包括将一系列现货价格分解成趋势季节性和随机成分，独立建模，然后结合其预测，可以产生更多比同一自回归模型校准到价格本身的方法准确的点预测。在这里，我们表明，当解释性变量（负载预测）也被终止时，可以实现进一步的准确性增益。更重要的是，考虑到瘢痕概念的小说扩展到概率预测和应用两种组合预测分布的方法，我们发现（i）疤痕型模型几乎总是显着优于自回归基准，但又通过组合的瘢痕预测表现优于表现优势， （ii）使用量子回归计算的预测分布平均（QRA）优于从历史模拟和自举方法获得的那些，而（III）通过预测分布的平均通常会产生比量子位的平均价格更好的概率预测。鉴于概率预测是一个与风险管理密切相关的概念，我们的研究对电力部门的风险官员和投资组合管理人员具有重要意义。 （c）2018年elestvier b.v.保留所有权利。

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