远期市场购电的人工智能解决方案
An Artificial Intelligence Solution for Electricity Procurement in Forward Markets
摘要：
居民和主要电力消费者通常提前几年在远期市场购买其估计电力需求的重要百分比。这一长期电力采购任务包括确定何时购买电力，以使由此产生的能源成本最小化，并涵盖预测的消费量。在这篇科学文章中，重点是比利时远期市场的年度基本负荷产品，名为日历（CAL），可在交割期前三年进行交易。本文介绍了一种新的算法，根据CAL价格的历史，提供现在购买电力或等待未来机会的建议。该算法依赖于深度学习预测技术和量化与完全统一的参考采购政策偏差的指标。平均而言，所提出的方法超过了基准采购政策的考虑，并实现了1.65%的成本降低相对于完全统一的参考采购政策实现平均电价。此外，除了自动化复杂的电力采购任务外，该算法多年来显示出更一致的结果。最后，所提出的解决方案的通用性使得它非常适合于解决其他商品采购问题。
英文摘要：
Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement policy. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement policy achieving the mean electricity price. Moreover, in addition to automating the complex electricity procurement task, this algorithm demonstrates more consistent results throughout the years. Eventually, the generality of the solution presented makes it well suited for solving other commodity procurement problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
