利用非凸正则化的量化回归平均法预测电价

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-03-05 DOI:10.1002/for.3103
He Jiang, Yao Dong, Jianzhou Wang
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引用次数: 0

摘要

电价预测(EPF)是一个新兴的研究领域,其重点是对未来电力市场价格进行确定性和概率性预测。随着电力市场管制的放松以及风能和太阳能等可再生能源的广泛应用,电价预测引起了从业人员和学者的极大兴趣。然而,由于电价的高波动性、随机性和波动性,准确有效地预测电价是一项极具挑战性的任务。虽然自 2014 年全球能源预测竞赛(GEFCom2014)以来,量化回归平均法(QRA)已被证明在概率 EPF 中是有效的,但它对干扰变量很敏感,尤其是当变量数量较多时。这些干扰变量会对预测精度产生负面影响。为了应对这些挑战,本研究探讨了概率预测中的非凸正则化 QRA。两种非凸正则化 QRA 选择了从点预测中获得的重要输入,以获得更准确的预测结果。为了证明所提出的 EPF 模型的有效性,本研究考虑了来自欧洲电力市场的两个真实数据集。
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Electricity price forecasting using quantile regression averaging with nonconvex regularization

Electricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity price accurately and efficiently is an extremely challenging task because of its high volatility, randomness, and fluctuation. Although quantile regression averaging (QRA) has been demonstrated to be efficacious in probabilistic EPF since the global energy forecasting competition in 2014 (GEFCom2014), it is sensitive to nuisance variables especially when the number of variables is large. The forecasting accuracy will be negatively affected by these nuisance variables. To address these challenges, this study investigates a nonconvex regularized QRA in probabilistic forecasting. Two types of nonconvex regularized QRA select the important inputs obtained from point forecasting to obtain more accurate forecasting outcomes. To demonstrate the effectiveness of the proposed EPF model, two real datasets from the European power market are considered.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
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.
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