AI vs. Physics: Who Forecasts Solar Production More Accurately?

AI vs. Physics: Who Forecasts Solar Production More Accurately?

Forecasting solar energy production is both a science and an art — especially for prosumers (like me) and grid operators who rely on accurate day-ahead predictions to plan consumption, storage, or grid feedback.

Broadly speaking, there are two dominant approaches:

• Physically-based models, which use environmental inputs like Global Tilted Irradiance (GTI), geographic orientation, panel specifications, and theoretical efficiency limits. These models are grounded in atmospheric physics and solar geometry, and they perform well under ideal, stable conditions.

• Machine Learning models, on the other hand, are trained on historical production data, weather forecasts, and meteorological trends. Instead of modeling the physical world from first principles, they learn from patterns in the past — including non-obvious correlations like morning humidity, temperature drops, or local shading quirks.

Curious to see which method was more accurate in a real-world residential solar installation, I built both: a GTI-based system and a machine learning pipeline, and ran them in parallel for several months.

What follows is a data-driven comparison of the two — where physics meets pattern recognition, and where empirical accuracy takes the stage.

The Challenge

Each day, my system logs: - Actual solar energy production (in kWh) - Forecasts from a GTI-based irradiance model - Forecasts from a trained AI model (either RandomForest or GradientBoosting)

We track both absolute and percentage errors, and keep a running tally of which model was closer.

The Results Are In

Out of more than 75 days of data, here’s what we found:

• AI models won 92% of the time – meaning their forecasts were closer to reality than GTI.
• GTI-based forecasts consistently overestimated energy production, sometimes by 30–50%.
• GradientBoosting performed extremely well in May and early June, later replaced by RandomForest, which maintained comparable accuracy.
• Most AI error margins were below 2%, with many forecasts falling within 1% of actual production.

A Few Notable Days

| Date | Actual (kWh) | AI Error (%) | GTI Error (%) | Winner | |------------|---------------|---------------|----------------|--------| | 2025-06-15 | 32.27 | 28.8% | 0.05%. | GTI (rare win)
| 2025-05-07 | 31.40 | 54.2% | 111% | Outlier – underestimated sun
| 2025-05-05 | 27.23 | 89.3% | 158.6% | Huge overestimation

Outliers aside, AI models showed remarkable consistency, with typical day-to-day errors smaller than the natural variability of solar input.

Why AI Wins

Physically-based GTI models assume ideal, unobstructed sun exposure, and have no way of accounting for: - Unexpected cloud cover - Dust, dirt, or shading - Inverter limitations or clipping

In contrast, AI learns from real-world conditions and corrects for those factors automatically — because it has seen them before in the data.

What’s Next?

Now that we have proof that AI is not only viable but superior, the next steps are: - Automating model selection based on recent performance (Meta-AI?) - Fine-tuning error detection on outlier days - Incorporating real-time feedback loops to retrain the model weekly

Final Verdict

AI-based forecasting models consistently outperform GTI in accuracy and reliability. They adapt, they learn, and they deliver tight predictions — even when the weather doesn’t play fair.

In the age of prosumer energy and smart grids, there’s no question: AI is the smarter sun-watcher.