Data science toolkit for energy grid machine learningS. Himmelstein | September 23, 2022
More than 100 million smart meters have been installed in the U.S. as of 2020. As the number of these meters and the demand for energy is expected to increase by 50% by 2050, so will the amount of voltage, current and electricity consumption data those smart meters produce. An open-source, data-science toolkit for power and data engineers has been developed by U.S. Lawrence Livermore National Laboratory to provide an integrated energy data storage and augmentation infrastructure.
The open source GridDS system provides an integrative software platform to train and validate machine learning models and is expected to help improve the efficiency of distributed energy resources, such as smart meters, batteries and solar photovoltaic units. The tool leverages advanced metering infrastructure, outage management systems data, supervisory control data acquisition and geographic information systems to forecast energy demands and detect incipient grid failures.
GridDS features a modular, generalizable Python software library for these multiple streams of data. In adapting to disparate datasets recorded by various devices, GridDS provides a range of unique functionalities not presently implemented in current advanced distribution management systems, which tend to have highly specific software infrastructure by design.