UM FRAMEWORK BASEADO EM DATA LAKEHOUSE PARA ARMAZENAMENTO, ANÁLISE E INTEGRAÇÃO DE DADOS EM SISTEMAS ENERGÉTICOS RENOVÁVEIS

Abstract

This dissertation proposes FIDLER, a Data Lakehouse-based framework for storing, integrating, and analyzing renewable energy data, focusing on wind and tidal resources in the state of Maranhão, Brazil. The research is driven by the need for efficient solutions to handle the heterogeneity, volume, and variety of data generated by measurement equipment such as LIDAR, SODAR, ADCP, and micrometeorological towers. The architecture is structured in Bronze, Silver, and Gold layers, preserving the temporal semantics of energy data while promoting governance, scalability, and integration with modern analytical tools. The study includes database extensions such as TimescaleDB and PostGIS to optimize queries and support georeferencing, and it defines specific strategies for ingesting, standardizing, and enriching the data. The framework is validated using real-world campaigns conducted in Maranhão, demonstrating its potential to support time-series analysis, generation forecasting, and decision-making for the regional energy transition.