Aggregation of multidimensional data for the decision support process for the management of microgrids with renewable energy sources
DOI:
https://doi.org/10.15587/2706-5448.2022.255957Keywords:
data processing and storage, data warehouse, database, data mart, triggersAbstract
The object of research is the process of processing and storing data when making decisions on managing the life cycle of electricity generation and consumption in microgrids with renewable energy sources. The prospects of the study are due to the fact that in order to provide a full-fledged decision support process in the management of microgrids with renewable energy sources, it is necessary to consolidate and manipulate multidimensional data in multithreading and online information processing. To solve the problem, the theoretical methods of analysis, abstraction, induction and deduction were used. To ensure multidimensionality and multithreading of data processing, it is proposed to develop a data warehouse based on the snowflake data model. Efficiency of information processing in real time is provided by an operational database built on the principle of OLTP. The organization of the joint work of the data warehouse with the operational database, the consolidation and manipulation of data is provided by triggers.
The result of the work is a data warehouse that will be used in the decision support system for managing energy microgrids, which will improve the efficiency of data processing and storage. This is achieved by combining the work of a centralized data warehouse with an operational database, as well as the use of a separate data mart for each user of the system. The practical significance of the work lies in the fact that the data warehouse will become part of the decision support system for processing information about the life cycle of energy in the management of energy infrastructure. Compared to using a single database for a decision support system, this approach ensures the speed of working with data and allows differentiating between the use of a data warehouse for analytics and data manipulation operations.
The data warehouse was deployed in a cloud environment on the Amazon Web Services (AWS) platform and the Amazon Relational Database Service (Amazon RDS) web service. Secure access to client data is implemented using data marts.
References
- Cardon, D. (2018). Database vs. Data Warehouse: A Comparative Review. Available at: https://www.healthcatalyst.com/insights/database-vs-data-warehouse-a-comparative-review/
- Han, J., Kamber, M., Pei, J. (2012). Data Mining Consepts and Techniques. Elsevier, 740. doi: http://doi.org/10.1016/c2009-0-61819-5
- Yang, Q., Ge, M., Helfert, M. (2019). Analysis of Data Warehouse Architectures: Modeling and Classification. Proceedings of 21st International Conference on Enterprise Information Systems, 604–611. doi: http://doi.org/10.5220/0007728006040611
- Li, Y., Wang, L., Ji, L., Liao, C. (2013). A Data Warehouse Architecture supporting Energy Management of Intelligent Electricity System. Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering, 696–699. doi: http://doi.org/10.2991/iccsee.2013.177
- Samsinar, R., Endro Suseno, J., Edi Widodo, C. (2018). Power Distribution Analysis For Electrical Usage In Province Area Using Olap (Online Analytical Processing). Proceedings of the 2nd International Conference on Energy, Environmental and Information System, 1–5. doi: http://doi.org/10.1051/e3sconf/20183111010
- Gökçe, H. U., Gökçe, K. U. (2014). Multi-dimensional energy monitoring, analysis and optimization system for energy efficient building operations. Sustainable Cities and Society, 10, 161–173. doi: http://doi.org/10.1016/j.scs.2013.08.004
- Sarka, D., Lah, M., Jerkic, G. (2014). Implementing a Data Warehouse with Microsoft SQL Server 2012. Microsoft Official Academic Course, 816.
- Levene, M., Loizou, G. (2003). Why is the snowflake schema a good data warehouse design? Information Systems, 28 (3), 225–240. doi: http://doi.org/10.1016/s0306-4379(02)00021-2
- Iqbal, M. Z., Mustafa, G., Sarwar, N., Wajid, S. H., Nasir, J., Siddque, S. (2020). A review of Star schema and Snowflakes Schema. Intelligent Technologies and Applications, 129–140. doi: http://doi.org/10.1007/978-981-15-5232-8_12
- Freedman, A. (2018). Dedicated vs. Cloud: Comparing dedicated and cloud infrastructure for high availability (HA) and non-high availability applications. Available at: https://silo.tips/download/dedicated-vs-cloud-comparing-dedicated-and-cloud-infrastructure-for-high-availab
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Copyright (c) 2022 Vira Shendryk, Yuliia Parfenenko, Olha Boiko, Sergii Shendryk, Yaroslava Bielka
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