PV Analytics

The prediction problem

Solar energy is great in theory. In practice, predicting how much power a panel will actually generate on any given day is surprisingly hard. Cloud cover, temperature, humidity, panel orientation, dust buildup: dozens of variables interact in non-obvious ways.

For solar plant operators, inaccurate forecasts mean either wasted capacity or unmet energy commitments. Both cost money.

Why neural networks

This was an academic project, but I approached it like a real business problem: can we predict daily solar output accurately enough to be useful for grid planning?

Traditional regression models struggle here because the relationships aren’t linear. A 10% increase in cloud cover doesn’t mean a 10% drop in output. It depends on cloud type, altitude, time of day, and a dozen other factors. Neural networks let the model learn those complex interactions instead of me trying to specify them manually.

What I built

A data pipeline in Python (Pandas, NumPy) to clean and merge weather station data with solar panel output records. Feature engineering to extract time-based patterns: hour of day, season, day length, weather interactions. Then a TensorFlow neural network trained on historical data to predict daily output, with Matplotlib visualizations showing predicted vs. actual, error distributions, and feature importance.

Results

• Built a working prediction model that outperformed linear regression baselines
• Demonstrated that neural networks captured the non-linear weather-energy relationships that simpler models missed

The real work was data cleaning. The weather data had gaps, the solar output data had sensor errors, and aligning timestamps across sources was its own mini-project. By the time I had clean, aligned data, the modeling part was almost straightforward.