APPLICATIONS OF BIG DATA IN RENEWABLE ENERGY SYSTEMS BASED ON CLOUD COMPUTING

This study examines the potential of microgrids (MG), which utilize renewable energy sources to provide sustainable power solutions. To conduct the analysis, we examined load and photovoltaic (PV) data, calculated minimum and maximum averages, and visualized the correlation using big data tools. We cleaned the data by removing unnecessary rows, merged the tables, converted them into CSV format, and uploaded them to the Databricks file distribution system (DBFS). Subsequently, we processed the data by creating a pipeline and using ETi (extract, transform, load) processes. We analyzed and visualized the data using tools such as Power BI and Tableau. The analysis identified the maximum and minimum PV production, assessed the impact of weather patterns on production, and measured the energy shortage between load demand and PV generation. Our research demonstrates the steps involved in handling and analyzing data, uploading it to the Hadoop ecosystem, transforming it into different file formats, connecting it to a relational database management system (RDBMS), and visualizing it using BI tools. In this study, we utilized cloud infrastructure to perform analytical tasks, including the use of business intelligence (BI) tools.