Unbundling Smart Meter Services Through Spatio-Temporal Decomposition Agents in DER-rich Environment

Smart meters and the advanced metering infrastructure (AMI) facilitate distribution system operators (DSO) to gather information on energy consumption at the customer level. With the increasing penetration of building-level intermittent distributed energy resources (DERs) behind the meter, DER information is not available to DSOs. At the same time, the smart meter enables users to participate in the grid with real-time information. Information for behind the meter is needed by the user to coordinate building level assets for maximum benefits. The concept of unbundled smart meter (USM) needs agents to decompose smart meter measurements to provide service to DSO and customers. In this paper, we propose a Spatio-TemporalDecomposition Agent (STDA) for USM based on Artificial Intelligence (AI) techniques. STDA can help users optimize their energy usage, help DSO to utilize building assets for the grid operation. The energy usage strategy developed by STDA is suitable for different users and can be customized by deep learning models according to the different energy consumption habits of each user. The power prediction performance results of various deep learning models and evaluation using a set of data from a Hawaii utility is presented. STDA pre-processes the measurements before model training and provides the Spatio-temporal decomposed forecasting.