Efficient mechanism for aggregate demand prediction in the smart grid

This paper studies an aggregate demand prediction problem relevant in smart grids. In our model, an aggregator agent is responsible for eliciting the demand forecasts of a number of self-interested home agents and purchasing electricity for them. Forecasts are given in form of probability distributions, and generating them incurs costs proportional to their precision. The paper presents a novel scoring rule based mechanism which not only makes the agents interested in reporting truthfully, but also inspires them to achieve the socially optimal forecast precision. Hence, the aggregator agent is then able to optimise the total expected cost of electricity supply. Therefore the mechanism becomes efficient, contrarily to prior works in this field. Empirical studies show that it is beneficial to join to the mechanism compared to purchasing electricity directly from the market, even if the mechanism consists only of a few agents.