<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper introduces a comprehensive smart grid big data solution, focusing on the processing and analysis of vast grid data to facilitate critical applications such as data resource management, real-time monitoring of grid conditions, and predictive load forecasting. Specifically, grid monitoring data are routed to distributed message queues, enhancing the indexing speed of real-time data access via the implementation of a B+ tree indexing algorithm. Furthermore, an optimized ant colony algorithm enhances the integration of big data with other advanced technologies, enabling efficient classification of diverse power information from multiple metering data sources. For empirical validation, data from national grid power meters were analyzed. Correlation analysis revealed that the correlation coefficients among smart meters 1, 5, and 15 are predominantly higher than 0.9. These coefficients tend to become more pronounced with time, delineating clearer connections and distinctions among the data from these meters. Additionally, the correlation between temperature and load values ranged between 0.91 and 0.98, significantly influencing daily load forecasts. The year 2023 saw an increase in the detection of online monitoring faults by 236 compared to 2020, underscoring the enhanced capabilities of smart grid condition maintenance. Moreover, monitoring data from various nodes of the national grid, with the exception of node 1#, exhibited deviation values ranging from 0.01 to 0.05, indicating high monitoring precision. In conclusion, the big data-driven approach to smart grid management presented in this study not only predicts load and performs state inspections efficiently but also holds significant practical value, suggesting a robust framework for future smart grid applications.</p>
</abstract>

This paper introduces a comprehensive smart grid big data solution, focusing on the processing and analysis of vast grid data to facilitate critical applications such as data resource management, real-time monitoring of grid conditions, and predictive load forecasting. Specifically, grid monitoring data are routed to distributed message queues, enhancing the indexing speed of real-time data access via the implementation of a B+ tree indexing algorithm. Furthermore, an optimized ant colony algorithm enhances the integration of big data with other advanced technologies, enabling efficient classification of diverse power information from multiple metering data sources. For empirical validation, data from national grid power meters were analyzed. Correlation analysis revealed that the correlation coefficients among smart meters 1, 5, and 15 are predominantly higher than 0.9. These coefficients tend to become more pronounced with time, delineating clearer connections and distinctions among the data from these meters. Additionally, the correlation between temperature and load values ranged between 0.91 and 0.98, significantly influencing daily load forecasts. The year 2023 saw an increase in the detection of online monitoring faults by 236 compared to 2020, underscoring the enhanced capabilities of smart grid condition maintenance. Moreover, monitoring data from various nodes of the national grid, with the exception of node 1#, exhibited deviation values ranging from 0.01 to 0.05, indicating high monitoring precision. In conclusion, the big data-driven approach to smart grid management presented in this study not only predicts load and performs state inspections efficiently but also holds significant practical value, suggesting a robust framework for future smart grid applications.

Real-time Access and Processing of Massive Measurement Data in Smart Power Grids

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Real-time Access and Processing of Massive Measurement Data in Smart Power Grids"}

This paper introduces a comprehensive smart grid big data solution, focusing on the processing and analysis of vast grid data to facilitate critical...

Real-time Access and Processing of Massive Measurement Data in Smart Power Grids

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 11. Feb. 2024

Akzeptiert: 21. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1479

Schlüsselwörter
Load forecasting, B+ tree indexing, Ant colony algorithm, Big data technology, Smart grid

© 2024 Xiaojiang Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Real-time Access and Processing of Massive Measurement Data in Smart Power Grids

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 11. Feb. 2024

Akzeptiert: 21. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1479

SchlüsselwörterLoad forecasting, B+ tree indexing, Ant colony algorithm, Big data technology, Smart grid

© 2024 Xiaojiang Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Load forecasting, B+ tree indexing, Ant colony algorithm, Big data technology, Smart grid