Data Science for Energy and the Environment

The energy and environmental sectors face increasingly complex challenges: managing limited resources, reducing environmental impact, and adapting to real-time changes in demand. Data Science makes it possible to address these challenges through large-scale data analysis, process automation, and accurate prediction of energy and environmental behavior.

This section showcases some of the projects carried out in the energy and environmental field, where the use of Big Data, Machine Learning, and advanced visualization has helped optimize operations, reduce costs, and move toward more sustainable models.

What types of projects have I developed?

• Energy consumption forecasting: Models that anticipate electricity demand in buildings, industries, or municipalities, helping to align production and reduce waste.
• Detection of inefficiencies and anomalies: Systems that identify abnormal behavior in distribution networks, equipment, or usage patterns, supporting efficiency improvements and loss prevention.
• Real-time environmental monitoring: Data analysis from sensors (IoT) to track emissions, air quality, noise levels, or water consumption.
• Optimization models for renewable resources: Predictive techniques to improve the management of solar, wind, or hydroelectric facilities.

Tools and technologies I use

For these projects, I work with languages such as Python and R, along with platforms like Google Cloud, AWS, Azure, and visualization tools like Power BI and Grafana. I also integrate data from sensors, public APIs, and SCADA systems to deliver a complete operational view.