GH IN
RENEWABLE ENERGY

Energy data.
Smarter decisions.

A series of data-driven tools and analyses for Canada's renewable energy sector — evaluating and forecasting solar, wind, and hydro performance, from functional studies to machine learning models.

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ABOUT

The cheapest power is already renewable

Renewable energy is becoming less of an option and more of an obvious decision. The world's growth demands more electricity, and traditional resources are proving more detrimental than beneficial in the long run.

Harnessing our abundant natural resources is no longer a question of if, but how efficiently, and recent advancements are closing the gaps that once held renewables back. Better forecasting reduces uncertainty, smarter grid integration handles variability, and storage systems like BESS turn intermittent generation into reliable, dispatchable power.

With reliable data and insights, we can measure what's performing, forecast what's coming, and model what it costs to get there. That's the foundation I built this website on.

Python Pandas / XGBoost Streamlit M&V / Regression LCOE Modeling RETScreen / PVWatts JavaScript / Chart.js
Wind turbines Solar panel farm Hydroelectric dam Transmission lines at dusk Wind turbine in green landscape Renewable energy Renewable energy Renewable energy Renewable energy Renewable energy Grid-scale battery storage containers at sunset with wind turbines Battery storage containers beneath transmission towers Aerial view of a battery storage facility at sunrise Tidal turbine platform deployed at sea Tidal energy platform with underwater turbines near Nova Scotia coastline
PROJECTS

Solar, wind, hydro, and the tools that tie them together

IN PROGRESS

On the drafting table

Projects currently in development — case studies, models, and tools not yet shipped.

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