Aplicação de técnicas de análise de dados utilizando PYTHON para avaliar e detectar oportunidades de melhoria em um sistema de geração distribuída.

Abstract

Clean energy generation has become increasingly popular in the realm of electricity distribution worldwide. This surge in popularity has enabled these forms of generation to reach not only large-scale power plants and corporations but also end consumers, allowing them to design and establish their own energy generation systems. Among these methods, photovoltaic energy generation has proven to be the most viable for this purpose. Furthermore, we are currently experiencing the so-called "Data Era." Data collected from various equipment and processes are crucial for assessing performance, efficiency, and identifying patterns that may indicate opportunities for enhancement. It is within the fusion of these two concepts that this study focuses on: analyzing and evaluating the behavior of distributed energy generation, such as photovoltaics, in a residential setting. To conduct this analysis, Python will be employed—a programming language increasingly prevalent in data analysis and evaluation, particularly with extensive datasets. Specific libraries such as Pandas, utilized for database manipulation, querying, and transformation, along with Matplotlib and Seaborn, employed for crafting visualizations and dashboards, and Numpy, for more intricate and advanced mathematical operations, will be utilized. Consequently, constructing this analysis as a report using the Jupyter framework will enable its availability not only in this study but also in online repositories like GitHub.