Prediction of the Share of Solar Power in China Based on FGM (1,1) Model
Abstract
:1. Introduction
2. Modeling Process of the FGM (1,1) Model and PSO Algorithm
2.1. Modeling Process of the FGM (1,1) Model
2.2. PSO Algorithm
3. Empirical Research
4. Results
5. Conclusions
6. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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MAPE Values | Accuracy Class |
---|---|
<10% | Excellent Class |
10–20% | Good Class |
20–50% | General Class |
>50% | Poor Class |
Year | Solar Power Generation (Billion KWh) | Total Power Generation (Billion KWh) | The Share of China’s Solar Power Generation (%) |
---|---|---|---|
2017 | 64.75 | 6275.82 | 1.03 |
2018 | 89.45 | 6791.42 | 1.32 |
2019 | 117.22 | 7142.21 | 1.64 |
2020 | 142.1 | 7417.04 | 1.92 |
Year | Original Data | GM (1,1) | FGM (1,1) (r = 0.3858) |
---|---|---|---|
2017 | 1.03 | 1.0300 | 1.0300 |
2018 | 1.32 | 1.3349 | 1.3246 |
2019 | 1.64 | 1.6032 | 1.6337 |
2020 | 1.92 | 1.9255 | 1.9212 |
MAPE | 0.91% | 0.20% |
Year | FGM (1,1) (r = 0.3858) | Year | FGM (1,1) (r = 0.3858) |
---|---|---|---|
2021 | 2.1906 | 2026 | 3.3784 |
2022 | 2.4460 | 2027 | 3.5967 |
2023 | 2.6906 | 2028 | 3.8110 |
2024 | 2.9265 | 2029 | 4.0220 |
2025 | 3.1553 | 2030 | 4.2301 |
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Li, Y.; Wang, S.; Dai, W.; Wu, L. Prediction of the Share of Solar Power in China Based on FGM (1,1) Model. Axioms 2022, 11, 581. https://doi.org/10.3390/axioms11110581
Li Y, Wang S, Dai W, Wu L. Prediction of the Share of Solar Power in China Based on FGM (1,1) Model. Axioms. 2022; 11(11):581. https://doi.org/10.3390/axioms11110581
Chicago/Turabian StyleLi, Yuhan, Shuya Wang, Wei Dai, and Liusan Wu. 2022. "Prediction of the Share of Solar Power in China Based on FGM (1,1) Model" Axioms 11, no. 11: 581. https://doi.org/10.3390/axioms11110581