Bidding in Non-Stationary Energy Markets

The PowerTAC competition has gained attention for being a realistic and powerful simulation platform used for research on retail energy markets, in part because of the growing number of energy markets worldwide. Agents in this complex environment typically use multiple strategies, changing from one to another, posing a problem for current learning algorithms. This paper introduces DriftER, an algorithm that learns an opponent model and tracks its error rate. We compare our algorithm in the PowerTAC simulator against the champion of the 2013 competition and a state of the art algorithm tailored for interacting against switching (non-stationary) opponents. The results show that DriftER outperforms the competition in terms of profit and accuracy.