The client needed a way to turn large volumes of raw energy data into a clear, actionable insights through interactive dashboard that could guide decision-making. With more than 4 million data points from 100+ households, the existing data was too complex to extract insights such as consumption patterns, solar production, battery needs, or grid connection options.

I designed and built REPA (Residential Energy Planning Assistant), a data analysis, simulation and visualization project powered by Python, analytics workflows, and interactive dashboards.

Data Cleaning & Processing: Handled millions of records, normalizing consumption, production, and resident behavioral data.

Interactive Dashboards: Created multiple pages for insights:

Analytics Features: Self-consumption ratio, performance benchmarking, scenario modeling.

The final solution transformed raw data into an interactive tool that helps homeowners and energy planners:

Quickly visualize energy data with clear charts and KPIs

Use simulation and A/B testing style comparisons to test scenarios

Identify the optimal battery size for maximum returns

Select the best grid connection for cost savings and stability

This project demonstrates my ability to combine data analytics, dashboard design, and Python workflows to deliver insights that are both visually clear and business-ready. Beyond energy planning, the same process applies to any business that wants to turn raw data into dashboards, simulations, and measurable growth insights.