论文标题
预测日期电价：对最先进算法，最佳实践和开放式基准测试的审查
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
论文作者
论文摘要
尽管在过去的二十年中，电力价格预测领域受益于大量贡献，但可以说，它缺乏评估新的预测算法的严格方法。通常使用独特的（不公开可用的数据集）比较后者，并且在太短且仅限于一个市场测试样本中。所提出的新方法很少针对建立良好的更简单模型的基准测试，精度指标有时不足，并且很少进行预测性能差异的重要性。因此，目前尚不清楚哪种方法的运行良好，而预测电价的最佳实践是什么。在本文中，我们通过对最先进的模型进行文献调查来解决这些问题，从而比较多年来和市场的最先进的统计和深度学习方法，并提出一系列最佳实践。此外，我们提供了所需的数据集，最新模型的预测以及专门设计的Python工具箱，以便在未来的研究中可以严格评估新算法。
While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new methods are rarely benchmarked against well established and well performing simpler models, the accuracy metrics are sometimes inadequate and testing the significance of differences in predictive performance is seldom conducted. Consequently, it is not clear which methods perform well nor what are the best practices when forecasting electricity prices. In this paper, we tackle these issues by performing a literature survey of state-of-the-art models, comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and by putting forward a set of best practices. In addition, we make available the considered datasets, forecasts of the state-of-the-art models, and a specifically designed python toolbox, so that new algorithms can be rigorously evaluated in future studies.
