Data-driven prediction and evaluation on future impact of energy transition policies in smart regions

Chunmeng Yang, Siqi Bu, Yi Fan, Wayne Xinwei Wan, Ruoheng Wang, Aoife Foley

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number120523
JournalApplied Energy
Volume332
DOIs
Publication statusPublished - 15 Feb 2023

Keywords

  • Energy transition
  • Machine learning
  • Policy evaluation
  • Policy prediction
  • Renewable energy

ASJC Scopus subject areas

  • Building and Construction
  • Renewable Energy, Sustainability and the Environment
  • Mechanical Engineering
  • General Energy
  • Management, Monitoring, Policy and Law

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