How to predict and optimise with asymmetric error metrics

TL;DR

This paper investigates how asymmetric error metrics impact the predict-then-optimize process, proposing adjustments to forecasts to improve decision-making costs in energy management scenarios.

Contribution

It introduces methods to adjust forecasts based on asymmetric loss functions, enhancing optimization outcomes in predict-then-optimize tasks.

Findings

Asymmetric errors significantly affect optimization costs.

Adjusting forecasts with tailored loss functions improves decision quality.

Positive correlation exists between forecast accuracy and optimization performance.

Abstract

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.