论文标题
如何使用不对称误差指标进行预测和优化
How to predict and optimise with asymmetric error metrics
论文作者
论文摘要
在本文中，我们研究了预测的概念并优化了问题，并特别参考了IEEE计算智能社会的第三次技术挑战。在这项比赛中，要求参赛者在六座建筑物和六个太阳能设备上预测建筑能源使用和太阳能生成，然后使用他们的预测在一个月内安排课程和电池时优化能源成本。我们研究了未预测和过度预测和不对称错误对优化成本的可能影响。我们探讨了预测和优化阶段损失函数的不同性质，并建议调整最终预测以获得更好的优化成本。我们报告说，尽管这两个之间存在正相关，但可以使用更合适的损失功能来优化与最终决策相关的成本。
In this paper, we examine the concept of the predict and optimise problem with specific reference to the third Technical Challenge of the IEEE Computational Intelligence Society. In this competition, entrants were asked to forecast building energy use and solar generation at six buildings and six solar installations, and then use their forecast to optimize energy cost while scheduling classes and batteries over a month. We examine the possible effect of underforecasting and overforecasting and asymmetric errors on the optimisation cost. We explore the different nature of loss functions for the prediction and optimisation phase and propose to adjust the final forecasts for a better optimisation cost. We report that while there is a positive correlation between these two, more appropriate loss functions can be used to optimise the costs associated with final decisions.
