结合电价的预测分布。在日前投标中,最小化CRPS是否会导致最优决策?

IF 0.7 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Weronika Nitka, Rafał Weron
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引用次数: 0

摘要

概率价格预测最近在电力交易中引起了人们的关注,因为基于这种预测的决策比仅凭点预测做出的决策产生的利润要高得多。同时,结合预测分布的方法正在开发,因为没有一个模型是完美的,平均通常可以提高预测性能。在本文中,我们讨论了使用CRPS学习(一种新颖的加权技术,最小化连续排名概率得分(CRPS))是否能在日前投标中产生最优决策的问题。为此,我们使用德国EPEX市场的每小时日前电价进行了实证研究。我们发现增加集合的多样性可以对精度产生积极的影响。与此同时,使用CRPS学习的较高计算成本与分布的等加权聚合相比,并没有被更高的利润所抵消,尽管预测明显更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining predictive distributions of electricity prices. Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?
Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article, we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we conduct an empirical study using hourly day-ahead electricity prices from the German EPEX market. We find that increasing the diversity of an ensemble can have a positive impact on accuracy. At the same time, the higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits, despite significantly more accurate predictions.
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来源期刊
Operations Research and Decisions
Operations Research and Decisions OPERATIONS RESEARCH & MANAGEMENT SCIENCE-
CiteScore
1.00
自引率
25.00%
发文量
16
审稿时长
15 weeks
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