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引用次数: 0
摘要
在放松管制的电力市场中,准确预测波动、非线性和高频率的电价对市场决策越来越重要。然而,与电价相关的不确定性,如非平稳性、非线性和高波动性,给电价预测(EPF)带来了严重困难。点预测只能提供对未来价格的单一、确定性估计,而概率预测则不同,它能更全面、更细致地反映未来的价格动态,从而帮助市场参与者在面临不确定性时做出更明智的决策。因此,在本文中,我们提出了一种用于多步骤概率预测的稳健深度学习方法。首先,我们在专家模型中使用最小绝对收缩和选择算子(LASSO)来生成点预测。其次,我们在时态融合变换器中引入了平滑剪切绝对偏差正则化项,这是一种非凸惩罚,在模型选择方面具有公认的神谕特性。最后,我们利用提出的模型整合点预测,给出概率预测。为了评估所提出的预测模型,我们在 Nord Pool 电力市场和波兰电力交易市场进行了真实数据实验。实证结果表明,与其他竞争者相比,所提出的模型具有卓越的概率预测性能,并在实际应用中证明了其有效性。
Probabilistic electricity price forecasting based on penalized temporal fusion transformer
In the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high-frequent electricity price for market decision-making. However, the uncertainties associated with electricity prices, such as non-stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better-informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi-step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real-data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real-world applications.
期刊介绍:
The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.