Open-source data-science toolkit to augment DER efficiency
A team at Lawrence Livermore National Laboratory (LLNL) has developed an open-source, data-science toolkit for power and data engineers to improve DER efficiency for smart meters, batteries and solar PV units.
The toolkit called GridDS provides an integrated energy data storage and augmentation infrastructure, as well as a flexible and comprehensive set of machine-learning models.
By providing an integrative software platform to train and validate machine learning models, GridDS aims to help improve the efficiency of distributed energy resources (DERs), such as smart meters, batteries and solar photovoltaic (PV) units.
LLNL cites the growing number of smart meters across the US, which will generate an immense amount of data. According to the LLNL, while energy standards have enabled large-scale data collection and storage, best practice methods of using this data to mitigate costs and consumer demand has been an ongoing focus of their research.
The GridDS is hoped to help make the most of such data.
“Until now, no open-source platforms have provided data integration or machine learning models. The few existing platforms have been proprietary and not available to the broader research community,” stated principal investigator and data scientist Indra Chakraborty at the Laboratory’s Center for Applied Scientific Computing (CASC).
“As an open-source toolkit, GridDS opens the door to data and power scientists everywhere who are working on these challenges and want to make the most of this data.”
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GridDS is designed to leverage advanced metering infrastructure (AMI), outage management systems data, supervisory control data acquisition and geographic information systems (GIS) to forecast energy demands and detect incipient grid failures.
The platform features a modular, generalisable Python software library for these multiple streams of data. In adapting to disparate datasets recorded by various devices, it provides a range of functionalities not presently implemented in current advanced distribution management systems (ADMS), which tend to have highly specific software infrastructure by design.
“Previous experiments have demonstrated that when it comes to applying the best machine learning model for a given energy problem, one shoe does not fit all. Each scenario is different, and context is key,” said Vaibhav Donde, associate program lead for Energy Infrastructure Modernization.
“We have found that researchers are better off trying several approaches to see what works best. With GridDS, you can make small tweaks to task designs, such as horizon or history in an autoregression, or carry over machine learning models between datasets, which enables learning transfer and broader model validation. GridDS can take general approaches, apply them to highly specific energy tasks and evaluate and validate their performance,” added Donde.
GridDS can also test several approaches to energy and sensor time-series problems and train model hyperparameters.
GridDS is available via Github and is funded by the Department of Energy’s Grid Modernization Lab Consortium (GMLC).
