Real-Time Solar Plant Performance Optimization Using AI and Weather Forecasts

A real-time solar plant performance optimisation system that uses AI and weather forecasts.

A real-time solar plant performance optimisation system that uses AI and weather forecasts. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are real-time processing of incoming data, data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses Apache Kafka, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.