Predicting Agent Trustworthiness for Large-Scale Power Demand Shifting

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 regarding power consumption shifting actions. In this paper we adopt two stochastic filtering techniques, a Gaussian Process Filter and a Histogram Filter, and use these to effectively monitor the trustworthiness of agent statements regarding their final shifting actions. We incorporate these within a directly applicable scheme for providing electricity demand management services. Experiments were conducted on real-world consumption datasets from Kissamos, a municipality of Crete. Our results confirm that these techniques provide tangible benefits regarding enhanced consumption reduction performance, and increased financial gains for the cooperative.