Prediction of the Share of Solar Power in China Based on FGM (1,1) Model

Li, Yuhan; Wang, Shuya; Dai, Wei; Wu, Liusan

doi:10.3390/axioms11110581

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…This was modeled on Germany's Renewable Energy Act of 2000, which used a feed-in tariff policy for solar power generation. Li, Wang, Dai, and Wu [28] showed that the Chinese government's support for the solar energy industry has given a strong impetus to the development of solar power generation.…”

Section: Renewable Energymentioning

confidence: 99%

Decision Rules for Renewable Energy Utilization Using Rough Set Theory

Huang,

Chen

et al. 2023

Axioms

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Rough Set (RS) theory is used for data analysis and decision making where decision-making rules can be derived through attribute reduction and feature selection. Energy shortage is an issue for governments, and solar energy systems have become an important source of renewable energy. Rough sets may be used to summarize and compare rule sets for different periods. In this study, the analysis of rules is an element of decision support that allows organizations to make better informed decisions. However, changes to decision rules require adjustment and analysis, and analysis is inhibited by changes in rules. With this consideration, a solution approach is proposed. The results show that not only can decision costs be reduced, but policymakers can also make it easier for the public to understand the incentives of green energy programs and the use of solar panels. The application process is simplified for the implementation of sustainable energy policies.

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Section: Renewable Energymentioning

confidence: 99%

Decision Rules for Renewable Energy Utilization Using Rough Set Theory

Huang,

Chen

et al. 2023

Axioms

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“…The specific weight allocation is calculated using fractional order, which can be determined using heuristic algorithms. Currently, the FGM(1,1) model has been widely utilized for various predictions, including energy consumption, environmental quality [20][21][22][23][24], express delivery business volume [25], the added value of high-tech industries [26], and the total output value of China's construction industry [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Corporate Innovation Resilience of China Based on FGM(1,1) and Fuzzy-Set Qualitative Comparative Analysis Model

Xia,

Jiao,

Wang

et al. 2023

Fractal Fract

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Over the past few years, the uncertain business environment has shaped the resilient development thinking of firms. Measuring and predicting innovation resilience plays a crucial role in fostering the sustainable development of enterprises. This paper used the entropy-weight TOPSIS model and FGM(1,1) model to measure the innovation resilience of companies based on an indicator system, covering aspects such as tolerance for factor scarcity, R&D safety, core technology self-sufficiency, and organizational change capacity. The results show that the MAPE of the FGM(1,1) model is 0.0136, which is lower than that of the GM(1,1) model, with the predicted annual growth rate of the resilience being −0.95% from 2020 to 2025. Consequently, the study investigated what policy configuration may improve innovation resilience using the fuzzy-set qualitative comparative analysis (fsQCA) model. It identified four policy configuration paths, of which the combination of a tax policy for an additional deduction of enterprise R&D expenses and an income tax reduction policy is an effective policy configuration. This research expands the application of the FGM(1,1) model and inspires managers to develop innovative policies to enhance corporate resilience.

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“…For the defects existing in the precision of the model, Wu et al [29] introduced the concept of fractional order into the grey sequence operator and grey forecasting model [30] and developed the FGM (1,1). In this model, every sequence is multiplied by a distinct fractional order [27,31]. This model has been successfully implemented in energy consumption prediction [32], carbon sink capability prediction [33], and other fields.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Blockchain Cryptocurrency Price Prediction Using Fractional Grey Model

et al. 2023

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Cryptocurrency prices have the characteristic of high volatility, which has a specific resistance to cryptocurrency price prediction. Therefore, the appropriate cryptocurrency price predictive method can help reduce the investment risk of investors. In this study, we proposed a novel prediction method using a fractional grey model (FGM (1,1)) to predict the price of blockchain cryptocurrency. Specifically, this study established the FGM (1,1) through the closing price of three representative blockchain cryptocurrencies (Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC)). It adopted the PSO algorithm to optimize and obtain the optimal order of the model, thereby conducting prediction research on the price of blockchain cryptocurrency. To verify the predictive precision of the FGM (1,1), we mainly took MAPE, MAE, and RMSE as the judging criteria and compared the model’s predictive precision with the GM (1,1) through experiments. The research results indicate that within the data range studied, the predictive accuracy of the FGM (1,1) in the closing price of BTC, ETH, and LTC has reached a “highly accurate” level. Moreover, in contrast to the GM (1,1), the FGM (1,1) outperforms predictive capability in the experiments. This study provides a feasible new method for the price prediction of blockchain cryptocurrency. It has specific references and enlightenment for government departments, investors, and researchers in theory and practice.

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Prediction of the Share of Solar Power in China Based on FGM (1,1) Model

Cited by 3 publications

References 20 publications

Decision Rules for Renewable Energy Utilization Using Rough Set Theory

Decision Rules for Renewable Energy Utilization Using Rough Set Theory

Research on the Corporate Innovation Resilience of China Based on FGM(1,1) and Fuzzy-Set Qualitative Comparative Analysis Model

A Novel Method of Blockchain Cryptocurrency Price Prediction Using Fractional Grey Model

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