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

被引:11
作者
Yang, Chunmeng [1 ]
Bu, Siqi [1 ,2 ,3 ]
Fan, Yi [4 ]
Wan, Wayne Xinwei [5 ]
Wang, Ruoheng [1 ]
Foley, Aoife [6 ]
机构
[1] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Peoples R China
[2] Hong Kong Polytech Univ, Policy Res Ctr Innovat & Technol, Hong Kong, Peoples R China
[3] Hong Kong Polytech Univ, Res Inst Smart Energy, Hong Kong, Peoples R China
[4] Natl Univ Singapore, Dept Real Estate, Singapore, Singapore
[5] Monash Univ, Dept Banking & Finance, Melbourne, Australia
[6] Queens Univ Belfast, Sch Mech & Aerosp Engn, Belfast, North Ireland
关键词
Energy transition; Renewable energy; Policy prediction; Policy evaluation; Machine learning; RENEWABLE ENERGY; ELECTRICITY-GENERATION; TECHNOLOGY DIFFUSION; SOUTH-KOREA; INVESTMENT; CHINA; MODEL; CONSUMPTION; REDUCTION; BENEFITS;
D O I
10.1016/j.apenergy.2022.120523
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:17
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