  To meet widely recognised carbon neutrality targets, over the last decade
metropolitan regions around the world have implemented policies to promote the
generation and use of sustainable energy. Nevertheless, there is an
availability gap in formulating and evaluating these policies in a timely
manner, since sustainable energy capacity and generation are dynamically
determined by various factors along dimensions based on local economic
prosperity and societal green ambitions. We develop a novel data-driven
platform to predict and evaluate energy transition policies by applying an
artificial neural network and a technology diffusion model. Using Singapore,
London, and California as case studies of metropolitan regions at distinctive
stages of energy transition, we show that in addition to forecasting renewable
energy generation and capacity, the platform is particularly powerful in
formulating future policy scenarios. We recommend global application of the
proposed methodology to future sustainable energy transition in smart regions.
