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In this paper, we examine the concept of the predict and optimise problemwith specific reference to the third Technical Challenge of the IEEEComputational Intelligence Society. In this competition, entrants were asked toforecast building energy use and solar generation at six buildings and sixsolar installations, and then use their forecast to optimize energy cost whilescheduling classes and batteries over a month. We examine the possible effectof underforecasting and overforecasting and asymmetric errors on theoptimisation cost. We explore the different nature of loss functions for theprediction and optimisation phase and propose to adjust the final forecasts fora better optimisation cost. We report that while there is a positivecorrelation between these two, more appropriate loss functions can be used tooptimise the costs associated with final decisions.

How to predict and optimise with asymmetric error metrics