Solar utilities rely on accurate sunlight forecasts to determine how much power they will produce on a given day. Clean Power Research provides these forecasts, but existing methods take too much time and computation to forecast for a given location immediately. To explore machine learning solutions, our Clinic team constructed a pipeline to retrieve and transform data, train predictive models and evaluate their performance. We used this to investigate new data sources and the viability of two different machine learning approaches.