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

Chunmeng Yang, Siqi Bu, Yi Fan (), Wayne Wan, Ruoheng Wang and Aoife Foley

Applied Energy, 2023, vol. 332, issue C, No S0306261922017809

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.

Keywords: Energy transition; Renewable energy; Policy prediction; Policy evaluation; Machine learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Working Paper: Data-Driven Prediction and Evaluation on Future Impact of Energy Transition Policies in Smart Regions (2022)
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DOI: 10.1016/j.apenergy.2022.120523

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