Work

Digital twin for renewable energy landscape planning and design

Year: 2025

Type: article

Abstract: To address the uncertainties and the integration of multisource data during landscape planning, we proposed a digital twin-based renewable energy landscape planning and design method. First, a probabilistic graph model combined with decision-coupled network was introduced to optimize modeling uncertainties and interrelationships. Then, a landscape planning recognition model was proposed to achieve feature matching between real and simulated scenes. And heterogeneous data assimilation was used to optimize the discrepancies between multimodal data. Experimental results proved that the proposed method outperformed existing methods in several key performance indicators, particularly in model accuracy and reliability, and it significantly reduced data assimilation errors.

Source: International Journal of Low-Carbon Technologies

DOI: 10.1093/ijlct/ctaf147

Author: Hao Zheng

Institution: Henan Agricultural University

Language: English

Topic: Integrated Energy Systems Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Climate action, Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,886