Data for Hierarchical time-series approaches for photovoltaic systems performance forecasting with sparse datasets

Predicting the performance of photovoltaic (PV) power plants and rooftop systems can often be challenging due to difficulties in data collection and incoherencies in interconnected systems. Following the hierarchical aggregation structure from geographical and temporal similarities between PV systems, we suggest a simplified approach to predicting the performance of individual installations and evaluating the impact of these hypothetical installations on the overall grid.

We use the hierarchical nature of power generation and ascertain weather datasets to predict the performance of new or existing systems for locations with unmeasured input data. We demonstrate an approach that could improve grid stability by using a hierarchical model on publicly available datasets on utility and rooftop installations. Ensemble machine learning algorithms are trained with 16 weeks of known hourly input training features to form a baseline model for known locations.