Core Objective 1: Central Data Resource
As a core objective, DuraMAT collects and disseminates photovoltaic (PV) module reliability related data, and applies data science to derive new insights. The purpose of a centralized data resource is to enable DuraMAT researchers to securely assimilate, access, analyze, and share data at a central repository, create outreach programs, and consolidate findings for our stakeholders.
DuraMAT data types include streaming time-series performance data, historical time-series data, static module and materials characterization, images, simulation outputs, accelerated testing data (time series and static), software tools, and analysis results.
The DuraMAT DataHub is a centralized data resource with the capability to ingest, curate, combine, analyze, and manage heterogeneous data. It allows researchers to analyze these different data types more effectively, especially when used with our open access software tools. With these tools, researchers can identify trends in fielded module performance and use detailed characterization techniques to get to the root of underlying degradation and/or performance issues. It is a critical asset for DuraMAT and the PV community. The DataHub is housed at NREL, with secure access fo rall DuraMAT partners and public access to published data.
Our long-term goals for the DataHub include the ability to link field module performance data with accelerated testing data and to establish a material property data bank for simulations and design.
Key Results
- Demonstrate a central data resource, the DuraMAT DataHuB, that securely hosts a mix of private and public data of multiple data types
- Development of open-source software libraries that apply machine learning to solve module reliability challenges leveraging the data available in the DataHuB.
- Demonstrated applications of the data and software tools to address short term commercial challenges that are beyond current industry capabilities and long-term research challenges
- Techno-economic analysis of the effects of more predictive accelerated testing, lower degradation, and resilient module designs and materials.
Related Projects
Climate Classifications for Photovoltaic Degradation
Data Cleaning for Degradation Analyses
PVPRO: Methods for Determining Photovoltaic Degradation from Maximum Power Production Data
Techno-Economic Analysis for Economic and Market Impact
Assessing Factors Underpinning PV Degradation through Data Analysis
Techno-Economic Analysis to Inform PV Performance, Cost, and Sustainability
Assessment of Accelerated Stress Testing Data Using Tensor Decomposition Methods
Contact
To learn more about this core objective, contact Anubhav Jain.
