A nonparametric Bayesian model for forecasting residential solar generation

Abstract

This paper presents a framework for generating synthetic residential solar generation profiles. Using the Dirichlet process, characteristics can be clustered and assigned to unobservable connections in a network. This approach retains the variance of this assignment introduced by sparse data, and allows for profiles to be generated specific to individual characteristics. It is also demonstrated that a Markov process modelling changing solar irradiance can be defined from existing solar generation data, rather than specific solar irradiance data in the event that it is not available. This model was applied to data sourced from Ausgrid's Smart Grid, Smart City Program, and a limited initial application found that profiles specific to assigned characteristics could be generated successfully.