A novel fractional-order accumulation grey power model and its application

Yang, Huijie; Gao, Mingyun; Xiao, Qinzi

doi:10.1007/s00500-022-07634-3

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…The flexibility of the power exponent makes the model a more flexible reflection of nonlinear features of real systems and thus can be used to describe a development process that exhibits a single peak feature. For the past few years, this model has also attracted the attention of many researchers [3][4]. To enhance the model's adaptive capacity of the GM(1,1) power model, researchers have conducted research from perspectives such as the instability of the GM(1,1) power model [5], optimization of power exponent, and background value interpolation coefficients [6].…”

Section: Introductionmentioning

confidence: 99%

The Discrete Grey Power Model Based on the New Information Priority and its Application

Yang,

Li,

Zhao

2023

J. Phys.: Conf. Ser.

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How to upgrade the performance and suitability of the forecasting method and reduce the modelling error for nonlinear small sample data series has been one of the most key problems in grey system theoretical exploration. The main work of this study, a discrete grey power model is formulated that can not only simulate the development of exponential and power function systems but can also reflect the interaction between them. The new model not only ensures that the more recent information is given some priority but exhibits a lower simulation error and greater prediction accuracy. The findings indicated that the presented model minimizes the average relative error in theory, with better adaptability than previous models in simulation and prediction. The case study demonstrates that the new model is more appropriate for predicting small sample data than the previous grey power model. This research addresses limitations in existing grey models and expands the grey model system.

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Section: Introductionmentioning

confidence: 99%

The Discrete Grey Power Model Based on the New Information Priority and its Application

Yang,

Li,

Zhao

2023

J. Phys.: Conf. Ser.

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“…Zhang et al (2022) and Sui and Qian (2021) predicted the future trends of renewable energy consumption in China, the United States, and Germany, as well as the trends of wind power genesis in China, respectively, with grey prediction models. Yang et al (2023) utilized grey power model to predict the supply chain demand of Chinese home appliances. These studies show that grey prediction method can effectively solve multivariate problems with unknown information.…”

mentioning

confidence: 99%

Prediction of intensity and scale variation trend of tropical cyclones combined the quantile regression with grey power model

Zhang,

Wang,

Amjad

et al. 2024

Preprint

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Global climate change has significantly altered the number and intensity of tropical cyclones in the Northwest Pacific Ocean, resulting in substantial losses for both residents and the economy in the southeast coastal regions of China, as well as affecting the operating efficiency and safety of offshore wind farms. Therefore, quantitatively analyzing the temporal characteristics of tropical cyclones and precisely predicting their trend is crucial for mitigating disasters in coastal countries. This paper analyzes the interannual variation characteristics of tropical cyclones making landfall in China from 1980 to 2022 based on the quantile regression method. Grey power model was also used to predict the quantile regression curves of key features of TCs making landfall in China in the next five normal, El Niño and La Niña years, and to speculate on the future changes of tropical cyclones in the northwestern Pacific Ocean with statistical data. The results show that the interannual pattern of maximum wind speeds of tropical TCs making landfall in China under the 0.1–0.9 quantile is significantly affected by the El Niño-Southern Oscillation (ENSO), and the maximum wind speeds of tropical cyclones in normal, El Niño, and La Niña years are all slightly increased and accompanied by an increase in the duration. The location of the landfalling TCs in normal and El Niño years is moving northeastward, and the latitude of the landfalling TCs in La Niña years is approaching 16 ° N. The predicted wind speeds of the TCs in normal and El Niño years are also slightly increased with the increase of the duration. From the quantile regression curves predicted using the gray power model, El Niño years will bring more extreme weather. Tropical cyclones making landfall in normal and El Niño years may decrease in the southern part of China and increase in the northern part, which may be related to the expansion of TCs due to global warming. The results of this research can provide useful references for climate change research, disaster prevention and mitigation, and related policy making in China.

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Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model

Xia,

Ren,

Wang

et al. 2024

Renewable Energy

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A novel fractional-order accumulation grey power model and its application

Cited by 5 publications

References 38 publications

The Discrete Grey Power Model Based on the New Information Priority and its Application

The Discrete Grey Power Model Based on the New Information Priority and its Application

Prediction of intensity and scale variation trend of tropical cyclones combined the quantile regression with grey power model

Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model

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