Analysis of China photovoltaic market development trend

In recent years, China is the world's largest PV producer, the most complete PV industry chain, the largest PV market and the largest PV power generation capacity. Under the dual-carbon target, the future development trend of China's PV market is very important. Based on the sample data of China's annual cumulative PV installations from 2001 to 2022, its regression equations were obtained by MATLAB software using cubic polynomials, exponential curves, Peel's growth curves, linderuo's growth curves, quadratic exponential smoothing, and BP neural network training models, respectively. After comprehensive analysis, the cubic polynomial curve and exponential curve fitting are better in terms of continuity, growth, reliability and rapidity, but the exponential method accelerates the error with the increase of extrapolation year, so it is recommended to use the cubic polynomial curve. Finally, the cubic polynomial is used to forecast the annual cumulative installed capacity of PV (including distributed and centralized) in China from 2023 to 2030. Comparing with the results predicted by the China Photovoltaic Industry Association (CPIA), the prediction results of this paper are on the small side, which is mainly due to the neglect of the effects of favorable policies, technological breakthroughs, and increasing domestic and foreign market demand

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Publication Details

Published
2024-08-16
DOI
https://doi.org/10.1117/12.3040000
Primary Topic
Energy Load and Power Forecasting
Type
article
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Analysis of China photovoltaic market development trend

Quanhong Yuan
Energy Load and Power Forecasting
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Analysis of China photovoltaic market development trend

Quanhong Yuan
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Abstract

In recent years, China is the world's largest PV producer, the most complete PV industry chain, the largest PV market and the largest PV power generation capacity. Under the dual-carbon target, the future development trend of China's PV market is very important. Based on the sample data of China's annual cumulative PV installations from 2001 to 2022, its regression equations were obtained by MATLAB software using cubic polynomials, exponential curves, Peel's growth curves, linderuo's growth curves, quadratic exponential smoothing, and BP neural network training models, respectively. After comprehensive analysis, the cubic polynomial curve and exponential curve fitting are better in terms of continuity, growth, reliability and rapidity, but the exponential method accelerates the error with the increase of extrapolation year, so it is recommended to use the cubic polynomial curve. Finally, the cubic polynomial is used to forecast the annual cumulative installed capacity of PV (including distributed and centralized) in China from 2023 to 2030. Comparing with the results predicted by the China Photovoltaic Industry Association (CPIA), the prediction results of this paper are on the small side, which is mainly due to the neglect of the effects of favorable policies, technological breakthroughs, and increasing domestic and foreign market demand

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Energy Load and Power Forecasting
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