Using Time-Series Databases for Energy Data Infrastructures
The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently invo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/21/5478 |
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| author | Christos Hadjichristofi Spyridon Diochnos Kyriakos Andresakis Vassilios Vescoukis |
| author_facet | Christos Hadjichristofi Spyridon Diochnos Kyriakos Andresakis Vassilios Vescoukis |
| author_sort | Christos Hadjichristofi |
| collection | DOAJ |
| description | The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture. |
| format | Article |
| id | doaj-art-08c68cc408954e4e945665d570db6ca9 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-08c68cc408954e4e945665d570db6ca92025-08-20T02:14:22ZengMDPI AGEnergies1996-10732024-11-011721547810.3390/en17215478Using Time-Series Databases for Energy Data InfrastructuresChristos Hadjichristofi0Spyridon Diochnos1Kyriakos Andresakis2Vassilios Vescoukis3Software Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, GreeceSoftware Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, GreeceElectric Energy Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, GreeceSoftware Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, GreeceThe management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture.https://www.mdpi.com/1996-1073/17/21/5478timeseries dataenergy markets dataenergy data infrastructures |
| spellingShingle | Christos Hadjichristofi Spyridon Diochnos Kyriakos Andresakis Vassilios Vescoukis Using Time-Series Databases for Energy Data Infrastructures Energies timeseries data energy markets data energy data infrastructures |
| title | Using Time-Series Databases for Energy Data Infrastructures |
| title_full | Using Time-Series Databases for Energy Data Infrastructures |
| title_fullStr | Using Time-Series Databases for Energy Data Infrastructures |
| title_full_unstemmed | Using Time-Series Databases for Energy Data Infrastructures |
| title_short | Using Time-Series Databases for Energy Data Infrastructures |
| title_sort | using time series databases for energy data infrastructures |
| topic | timeseries data energy markets data energy data infrastructures |
| url | https://www.mdpi.com/1996-1073/17/21/5478 |
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