ETL Pipeline and Machine Learning Analysis to forecast Renewable Energy Generation in Germany using Weather Data
This project is a work in progress.
This portfolio project aims to explore the integration and analysis of renewable energy generation and consumption data alongside weather data for Germany, focusing on the timeframe after 2010. Utilizing both SQL (MySQL) and NoSQL (MongoDB) databases, the project seeks to uncover insights into how weather impacts energy dynamics, identify energy consumption patterns, and evaluate renewable energy sources' efficiency.
- Renewable Energy Data: From ENTSO-E Transparency Platform. Data retrieved through the websites API.
- Weather Data: Detailed records of over a hundred weather stations throughout Germany from the Deutscher Wetterdienst (DWD) Climate Data Center, including temperature, solar radiation, and wind speed metrics. Data retrieved through webscraping.
- Databases: MongoDB for unstructured data; MySQL for structured data.
- Programming: Python, with libraries such as pandas for data processing. SQL to interact with the databases.