On the Use of Off-the-Shelf Machine Learning Techniques to Predict Energy Demands of Power TAC Consumers

Abstract

The Power Trading Agent Competition (Power TAC) is a feature-rich simulation that simulates an energy market in a smart grid, where software brokers can buy energy in wholesale markets and sell energy in tariff markets to consumers. Successful brokers can maximize their profits by buying energy at low prices in the wholesale market and selling them at high prices to the consumers. However, this requires that the brokers have accurate predictions of the energy consumption of consumers so that they do not end up having excess energy or insufficient energy in the marketplace. In this paper, we conduct a preliminary investigation that uses standard off-the-shelf machine learning techniques to cluster and predict the consumption of a restricted set of consumers. Our results show that a combination of the popular k-means, k-medoids, and DBSCAN clustering algorithm together with an autoregressive lag model can predict, reasonably accurately, the consumption of consumers.