Initial Exploration of Machine Learning to Predict Customer Demand in an Energy Market Simulation

摘要

The PowerTAC competition focuses on trading activities in energy markets. One of the important subtasks of designing an effective agent for this scenario is to predict the energy use and generation of the customer agents in the marketplace. These predictions can inform pricing and tariff design questions, as well as decisions to balance power use and generation over time. Similar prediction problems are also important in real world energy markets. Here we present some initial experiments applying machine learning to predict future customer energy usage patterns in the PowerTAC simulation.