Hierarchical Time-Series Approaches for Photovoltaic System Performance Forecasting With Sparse Datasets

Abstract

Solar-based power generation presents challenges for system and grid operators due to the intermittent nature of power supply. 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. The prediction accuracy is then directly compared for locations with known and unknown input features, both on a granular and subregion level. We observe a reduction in prediction accuracy by 6–8% using the hierarchical approach. The accuracy of the hierarchical model can be further enhanced beyond our work by increasing the training dataset temporally, as well as by augmenting nested layers of the hierarchy.