Aggregation of multidimensional data for the decision support process for the management of microgrids with renewable energy sources

Vira Shendryk, Yuliia Parfenenko, Olha Boiko, Sergii Shendryk and Yaroslava Bielka
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Vira Shendryk: Sumy State University
Yuliia Parfenenko: Sumy State University
Olha Boiko: Sumy State University
Sergii Shendryk: Sumy National Agrarian University
Yaroslava Bielka: Sumy State University

Technology audit and production reserves, 2022, vol. 2, issue 2(64), 16-20

Abstract: 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.

Keywords: data processing and storage; data warehouse; database; data mart; triggers (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:baq:taprar:v:2:y:2022:i:2:p:16-20

DOI: 10.15587/2706-5448.2022.255957

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