Using Time-Series Databases for Energy Data Infrastructures

Christos Hadjichristofi, Spyridon Diochnos, Kyriakos Andresakis and Vassilios Vescoukis ()
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Christos Hadjichristofi: Software Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece
Spyridon Diochnos: Software Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece
Kyriakos Andresakis: Electric Energy Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece
Vassilios Vescoukis: Software Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece

Energies, 2024, vol. 17, issue 21, 1-23

Abstract: 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.

Keywords: timeseries data; energy markets data; energy data infrastructures (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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