Work

A novel digital-twin approach based on transformer for photovoltaic power prediction

Year: 2024

Type: article

Abstract: The prediction of photovoltaic (PV) system performance has been intensively studied as it plays an important role in the context of sustainability and renewable energy generation. In this paper, a digital twin (DT) model based on a domain-matched transformer is proposed using convolutional neural network (CNN) for domain-invariant feature extraction, transformer for PV performance prediction, and domain adaptation neural network (DANN) for domain adaptation. The effectiveness of the proposed framework is validated using a PV power prediction dataset. The results indicate an accuracy improvement of up to 39.99% in model performance. Additionally, experiments with varying numbers of timestamps demonstrate enhanced PV power prediction performance as parameters are continuously updated within the DT framework, offering a reliable solution for real-time and adaptive PV power forecasting.

Source: Scientific Reports

DOI: 10.1038/s41598-024-76711-4

Author: Xi Zhao

Institution: National University of Singapore

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

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

Open Access status: gold

Estimated APC paid: $2,590