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The study aims to develop an integrated framework for photovoltaic power generation that includes power forecasting and carbon assessment.

May 3, 2026Frontiers in Environmental Science0 citationsOpen Access

Research on photovoltaic power generation based on multi-dimensional indicators and models

PFPengying FanCapital University of Economics and Business ZCZhenlin ChenLingnan University YWYile WangBeijing Technology and Business University

Puntos clave

The study aims to develop an integrated framework for photovoltaic power generation that includes power forecasting and carbon assessment.
Developed a framework combining power forecasting, optimization, and carbon assessment.
Utilized methods such as PCA, t-SNE, and PSO for analysis and modeling.
Assessed carbon emission reductions in the context of China's power sector.
A 1% increase in PV generation could reduce carbon emissions by 2.05% by 2035.
The model achieved a coefficient of determination R² = 0.9975.

Resumen

Introduction Photovoltaic (PV) power generation is vital for sustainable energy and carbon reduction, yet existing studies often focus on single aspects, lacking integrated planning support. Methods This study develops a framework combining power forecasting, optimization, and carbon assessment using a multidimensional indicator system, PCA, t-SNE, and PSO. Results A 1% increase in PV generation could reduce China’s power sector carbon emissions by 2.05% by 2035; the model achieved an R 2 =0.9975. Discussion The framework supports regional energy planning, though future models should incorporate policy shifts and market dynamics.

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