Case Study
Renewcast · Solar Forecasting Product
Re-architecting day-ahead and intraday solar forecasts with a physics-aware core, optimisation engine, and ML adapters that delivered a 2.5× accuracy boost.
Industry
Utility-scale & distributed solar
Partner
Renewcast
Summary
As the sole AI Engineer and Data Scientist for Renewcast, I rebuilt the solar forecasting stack from the ground up -combining interpretable physical models, mathematically rigorous optimisation, and adaptive machine learning. Over a five-month continuous improvement program, each sprint stacked measurable gains until the platform shifted from reactive, error-prone forecasts to a physics-aware system trusted by trading and operations teams.
Portfolio nMAE
15% → 6%
2.5× more accurate
Error Reduction
60%
Lower imbalance cost
Sites Covered
100s
Utility & C&I
Delivery Timeline
5 months
Concept → production
Approach
Each workstream ran in overlapping, two-week sprints so the full physics core, optimisation engine, and ML adapters could hit production inside the five-month rebuild window -treating the engagement as a continuous improvement loop rather than a one-off rebuild.
1. Physical foundations
Modelled clear-sky irradiance, tracker geometry, temperature losses, shading, and site topology to create a physics baseline that traders could interrogate. The deterministic layer ensures forecasts remain interpretable while capturing site-specific behaviour such as single-axis tracker stow positions and historical curtailment.
2. Optimisation engine
Built a rolling-origin backtesting framework aligned to trading hours, with loss functions weighted by market penalties. Bayesian optimisation tuned hyperparameters per site, and feature selection routines guarded against weather bias drift -all orchestrated through MLflow tracking and automated reports.
3. Advanced ML layer
Layered a global gradient-boosted model with site-specific adapters and residual neural nets to correct remaining bias. Quantile regression delivered calibrated P50/P90 forecasts, enabling risk-aware bidding and reserve planning for every asset.
Performance evolution
Within five months, portfolio error dropped from 15% to 6% nMAE through an uninterrupted cadence of improvements. Explore the monthly glide path below -the chart highlights how each optimisation sprint compressed error bands and built confidence for traders.
Portfolio nMAE
6.2%
| Month | nMAE (%) |
|---|---|
| 2025-05 | 15.3 |
| 2025-06 | 11.1 |
| 2025-07 | 10.1 |
| 2025-08 | 9.4 |
| 2025-09 | 7.4 |
| 2025-10 | 6.2 |
New capabilities in progress
With the five-month continuous-improvement phase live, the next wave targets resilience modules that extend the same tempo to new product surfaces.
Stow-aware & wind-aware forecasting
Building high-resolution wind models (6 m height) with stow-position forecasts for Normal, East, West, and Flat tracker modes. These modules anticipate defence operations and quantify expected production loss under high-wind events.
Nowcasting & area forecasting
Rolling out 0–6 hour nowcasts that fuse satellite imagery with ground telemetry, plus regional aggregation models that capture shared cloud regimes across fleets to strengthen multi-site trading positions.
Integration & delivery
- CadenceDay-ahead & intraday refreshes
- Resolution15-minute intervals
- DeliveryAPI, SFTP, Renewcast portal
- StackPython · Xarray · LightGBM · PyTorch · MLflow · Docker · CI/CD
- TimelineConcept to production in 5 months
Why it matters
- 60% fewer forecasting errors that reduce imbalance costs and contract penalties.
- 2.5× accuracy uplift that strengthens trading signals and operational planning confidence.
- 5-month turnaround proving the team can deliver transformational forecasting upgrades without multi-year programmes.
- Physics + ML synergy delivering explainable forecasts at scale across diverse assets.
Let’s talk
Ready to transform your solar forecasts with physics-aware machine learning? I’d love to hear about your challenges and explore how we can replicate these gains.
Book a short assessment →