Solar PV Maps for Estimation and Forecasting of Distributed Solar Generation (Proposals Track)
Julian de Hoog (The University of Melbourne); Maneesha Perera (The University of Melbourne); Kasun Bandara (The University of Melbourne); Damith Senanayake (The University of Melbourne); Saman Halgamuge (University of Melbourne)
Rapid uptake of distributed solar PV is starting to make the operation of grids and energy markets more challenging, and better methods are needed for measuring and forecasting distributed solar PV generation across entire regions. We propose a method for converting time series data from a number of point sources (power measurements at individual sites) into 2-dimensional maps that estimate total solar PV generation across large areas. These maps may be used on their own, or in conjunction with additional data sources (such as satellite imagery) in a deep learning framework that enables improved regional solar PV estimation and forecasting. We provide some early validation and results, discuss anticipated benefits of this approach, and argue that this method has the potential to further enable significant uptake of solar PV, assisting a shift away from fossil fuel-based generation.