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.

中文翻译：

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使用分位数回归平均和非凸正则化进行电价预测

电价预测（EPF）是一个新兴的研究领域，重点是确定性和概率性地预测未来电力市场价格。自电力市场放松管制以及风能和太阳能等可再生能源的广泛应用以来，EPF引起了从业者和学者的极大兴趣。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。尽管自 2014 年全球能源预测竞赛（GEFCom2014）以来，分位数回归平均（QRA）已被证明在概率 EPF 中有效，但它对干扰变量很敏感，尤其是当变量数量很大时。这些干扰变量将对预测准确性产生负面影响。为了应对这些挑战，本研究研究了概率预测中的非凸正则化 QRA。两种类型的非凸正则化 QRA 选择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，考虑了欧洲电力市场的两个真实数据集。