Artículo

Exploring low-carbon and sustainable urban transformation design using ChatGPT and artificial bee colony algorithm

Resumen

The aim of this study is to provide effective solutions to promote the transition of resource-based cities to low carbon and sustainable development. Firstly, this study investigates the background of low-carbon transformation of resource-based cities. Secondly, it introduces the application method of Chat Generative Pre-trained Transformer (ChatGPT) in detail. Finally, this study proposes a comprehensive application of ChatGPT and artificial bee colony (ABC) algorithm. The results show that the average energy utilization efficiency improvement index of the group using ChatGPT is 0.11. The average energy efficiency improvement index of the group using ABC algorithm is 0.02 higher than that of the control group. The integrated application of ChatGPT and ABC algorithm can further improve the low-carbon transformation effect of resource-based cities and achieve the goal of green development.
Yu, Shuhui (58311069300); Yang, Ya (58134041400); Li, Jiamin (58885279800); Guo, Keyu (58885067700); Wang, Zeyu (57751418200); Liu, Yuwei (58884857300)
Exploring low-carbon and sustainable urban transformation design using ChatGPT and artificial bee colony algorithm
2024
10.1057/s41599-024-02765-4
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184878224&doi=10.1057%2fs41599-024-02765-4&partnerID=40&md5=c3f17fe3d02471dd2cfcb84745c6c7ba
School of Creativity and Design, Guangzhou Huashang College, Guangzhou, 511300, China; Faculty of Innovation and Design, City University of Macau, 999078, Macao; School of Architecture and Urban Planning, Anhui Jianzhu University, Hefei, 230601, China; Westminster School of Arts, University of Westminster, London, HA1 3TP, United Kingdom; School of Chinese Language and Literature, Wuhan University, Wuhan, 430072, China; School of Public Administration, Guangzhou University, Guangzhou, 510006, China; School of Arts and Communication, China University of Geosciences, Wuhan, 430074, China
All Open Access; Gold Open Access
Scopus
Artículo obtenido de:
Scopus
Fuente
0 0 votos
Califica el artículo
Subscribirse
Notificación de
Campus Samborondón
Vía Samborondón Km. 13.5
Call Center: 5150400
metricas@ecotec.edu.ec
Facebook Instagram Youtube

Mapa del sitio