Work

Multi-Data Source Feature Extraction and Spatio-Temporal Data-Driven Optimization Decision for Photovoltaic Power Station Site Selection

Year: 2025

Type: conference-paper

Abstract: This study presents a novel method for photovoltaic power station site selection via multi-data source feature extraction and spatio-temporal with data-driven optimization. By integrating high-resolution digital elevation models, we combine satellite data, and ground-based meteorological sensors measurements to estalish a dynamic solar irradiance estimation algorithm. Under a two-stage robust uncertainty optimization, a practice framework is employed to coordinate demand response, energy storage systems, and grid interactions. The experimental results shows that the operational costs have been reduced by 12 to 18% and the fault prediction reached 92%. The apporach provides an actionable policy insights for enhancing demand response participation and advancing adaptive decision-making tools for polar-volatile operating selection environments.

Source: International Conference on Computer Information and Big Data Applications (CIBDA)

DOI: 10.1145/3746709.3746823

Authors: Yu Sun, Xiaohu Fan, Xuejiao Pang, Haining Nie, Ying Song

Institutions: Wuhan City Polytechnic, Wuhan College

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

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

Open Access status: hybrid

OpenAlex ID: W4413812446

FWCI:

Cites: 15

Cited by:

Related to: 10