Incentives for Accurate Energy Predictions: How to Reduce Epistemic Uncertainty

Abstract

Accurate predictions of power fluctuations are pivotal to the operation of flexibility markets. While the design of flexibility markets is an active and ongoing field of research, the question of how to elicit high quality predictions in a non-cooperative setting is often overlooked. Conceptually, we contribute the concept of best prediction incentivizing contracts. Under such contracts the best response of an agent is to report the true distribution of its power fluctuation. This concept differs from Incentive Compatibility by explicitly taking epistemic uncertainty into account: while Incentive Compatible mechanisms often assume the agent possess perfect knowledge of their own valuation, our concept incentivizes agents to reduce their epistemic uncertainty about the world. In practical terms, we present generic closed form solutions for polynomial distributions and show they can be used to approximate realistic Gaussian distributions. Lastly, placing our work in a larger context, we show that third party agents can profit from providing improved predictions via arbitrage.