How to predict and optimise with asymmetric error metrics
This addresses energy cost optimisation for building managers, but appears incremental as it builds on existing competition frameworks.
The paper tackles the predict-and-optimise problem in building energy management by analyzing how asymmetric forecasting errors affect optimisation costs, proposing to adjust forecasts with better loss functions to reduce costs.
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.