水库水电报价策略优化选择的梯度推进法
A gradient boosting approach for optimal selection of bidding strategies in reservoir hydro
摘要：
电力生产商使用各种决策支持系统来管理和计划日前电力市场的销售，他们经常面临着选择任何给定日期最有利的投标策略的挑战。直到斑点清除之后才知道最优解。所使用的模型和策略的结果及其对盈利能力的影响可以连续记录，也可以使用历史数据进行模拟。访问越来越多的数据为机器学习模型的应用打开了大门，以预测任何一天的模型和策略的最佳组合。在这篇文章中，我们结合领域知识和机器学习技术（梯度提升和神经网络）分析了两种给定投标策略在几年内的历史表现。广泛的变量访问的模型之前，投标进行了评估，以预测最优策略为给定的一天。结果表明，机器学习模型可以学习稍微优于静态策略，其中基于整体历史表现选择一种投标方法。
英文摘要：
Power producers use a wide range of decision support systems to manage and plan for sales in the day-ahead electricity market, and they are often faced with the challenge of choosing the most advantageous bidding strategy for any given day. The optimal solution is not known until after spot clearing. Results from the models and strategy used, and their impact on profitability, can either continuously be registered, or simulated with use of historic data. Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategy for any given day. In this article, historical performance of two given bidding strategies over several years have been analyzed with a combination of domain knowledge and machine learning techniques (gradient boosting and neural networks). A wide range of variables accessible to the models prior to bidding have been evaluated to predict the optimal strategy for a given day. Results indicate that a machine learning model can learn to slightly outperform a static strategy where one bidding method is chosen based on overall historic performance.
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。
