Work

Optimized prediction of electrical and thermal performance in solar PV/T systems using cloud-based data acquisition and advanced ANN models

Year: 2025

Type: article

Abstract: This study develops a cloud-monitored platform for predicting electrical and thermal performance in photovoltaic/thermal systems using back propagation (BP) and radial basis function (RBF) models. Genetic algorithm (GA) and particle swarm optimization (PSO) were applied to optimize the BP and RBF models. Results show that all four models exhibit high prediction accuracy, with the GA-BP model achieving the best performance. The average prediction errors for electrical power, electrical efficiency, thermal storage tank temperature, and thermal efficiency were 0.74%, 0.97%, 0.47%, and 0.32%, respectively, significantly improving precision over conventional models.

Source: International Journal of Low-Carbon Technologies

DOI: 10.1093/ijlct/ctaf071

Authors: Yibing Zhang, Congcong Wang, Junhui Sun, Hongbing Chen, Pingjun Nie

Institutions: Beijing University of Civil Engineering and Architecture, China Construction Sixth Engineering Bureau Corp., Ltd (China)

Language: English

Topic: Solar Thermal and Photovoltaic Systems

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

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

Open Access status: gold

Estimated APC paid: $2,886