Stochastic Filtering Methods for Predicting Agent Performance in the Smart Grid

A variety of multiagent systems methods has been proposed for forming cooperatives of interconnected agents representing electricity producers or consumers in the Smart Grid. One major problem that arises in this domain is assessing participating agents uncertainty, and correctly predicting their future behaviour. In this paper, we adopt two stochastic filtering techniques —the Unscented Kalman Filter equipped with Gaussian Processes, and the Histogram Filter— and use these to effectively monitor the trustworthiness of agent statements regarding their final actions. The methods are incorporated within a directly applicable scheme for providing electricity demand management services. Simulation results confirm that these techniques provide tangible benefits regarding enhanced consumption reduction performance, and increased financial gains.