Forecast Hourly Energy Demand for a Microgrid Operator
Overview
What this challenge is about.
Forecast Hourly Energy Demand for a Microgrid Operator. Intermediate challenge in code. Writing production code that solves real engineering problems, earn a...
The Brief
What you'll do, and what you'll demonstrate.
Build a 48h probabilistic energy-demand forecaster with calibrated intervals and a dispatcher-friendly UI.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Build probabilistic forecasts (not just point estimates)
- Evaluate forecasts with pinball loss and coverage
- Translate interval forecasts into a usable dispatcher UI
- Document the integration with grid-import bidding
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Probabilistic Forecasting
Apply probabilistic forecasting to solve real industry problems and demonstrate production-level capability.
- Quantile Regression
Apply quantile regression to solve real industry problems and demonstrate production-level capability.
- Deep Forecasting
Apply deep forecasting to solve real industry problems and demonstrate production-level capability.
- Calibration
Apply calibration to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Dashboard Design
Apply dashboard design to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Machine Learning Engineer
Probabilistic forecasting with calibrated intervals and a shipped operator UI is exactly the day-to-day work of MLEs in energy and climate tech.
This challenge sharpens
- probabilistic-forecasting
- quantile-regression
- calibration
Data Scientist
Pinball-loss-based evaluation with sliced coverage is bread-and-butter senior data-science work on forecasting problems.
This challenge sharpens
- probabilistic-forecasting
- calibration
- quantile-regression
AI Engineer
Wiring a probabilistic forecaster into a dispatcher tool is the integration craft that AI engineers ship at climate-tech startups.
This challenge sharpens
- dashboard-design
- deep-forecasting
- python