Abstract
Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same domain, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications, which rely on large quantities of data from multiple sources. To address these challenges, the open-source Time-Series Data Pipelines (Tsdat) Python framework was developed by Pacific Northwest National Laboratory, with strategic guidance and direction provided by the National Renewable Energy Laboratory and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the marine energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat framework and the data standards within which it operates. A beta version of Tsdat has been released and is being used by several projects in marine energy, wind energy, and building energy systems.
Original language | American English |
---|---|
DOIs | |
State | Published - 2022 |
Event | OCEANS 2021: San Diego - Porto - San Diego, United States Duration: 20 Sep 2021 → 23 Sep 2021 |
Conference
Conference | OCEANS 2021: San Diego - Porto |
---|---|
Country/Territory | United States |
City | San Diego |
Period | 20/09/21 → 23/09/21 |
Bibliographical note
See NREL/CP-5700-80561 for preprintNREL Publication Number
- NREL/CP-5700-82499
Keywords
- Big data
- Data lake
- Data standards
- Interoperability
- Open-source
- Python