Forecasting Renewable Energy Consumption Using a Novel Fractional Grey Reverse Accumulation Model

Systems, Journal Year: 2025, Volume and Issue: 13(1), P. 51 - 51

Published: Jan. 15, 2025

The accumulation operation is the most fundamental method for processing data in grey models, playing a decisive role accuracy of model predictions. However, traditional forward does not adhere to principle prioritizing new information. Therefore, we propose novel fractional reverse accumulation, which increases coefficient fully utilize information carried by latest data. This led development model, termed FGRM(1,1). was validated using renewable energy consumption from France, Spain, UK, and Europe, results demonstrated that FGRM(1,1) outperformed other models terms simulation error, prediction comprehensive error. predictions indicated significant growth France moderate robust Europe overall. These findings highlight effectiveness proposed utilizing provide insights into transition emission reduction potential Europe.

Language: Английский

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A Nonlinear Multivariate Grey Bernoulli Model for Predicting Innovation Performance in High-Tech Industries

Communications in Nonlinear Science and Numerical Simulation, Journal Year: 2025, Volume and Issue: unknown, P. 108636 - 108636

Published: Jan. 1, 2025

Language: Английский

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Assessing numerical error bound of classic grey prediction model: An application to the transport performance of China’s civil aviation industry

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127103 - 127103

Published: March 1, 2025

Language: Английский

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Fractional Order Grey Model of Optimization Investment Allocation for Maximum Value Addition in Beijing’s High-Tech Industries

Fractal and Fractional, Journal Year: 2025, Volume and Issue: 9(4), P. 262 - 262

Published: April 19, 2025

High-tech industries are of strategic importance to the national economy, and Beijing has been designated as a science technology innovation center by State Council. Accurate analysis its added value is crucial for technological development. While recent data enhance prediction accuracy, limited volume poses challenges. The cumulative grey Lotka–Volterra model differential dynamic multivariate address this leveraging short-term effectively. This study applies these two models analyze influencing factors predict Beijing’s high-tech industry growth. Results show competitive relationship with four systems, lacking synergy. In next five years, mutually beneficial trend expected. Mean Absolute Percentage Error (MAPE) remains within 10%, confirming model’s reliability.

Language: Английский

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Bottom-up discrete systematic modelling for analysis and prediction of future trends for land-sea environmental pollution systems

Applied Mathematical Modelling, Journal Year: 2024, Volume and Issue: unknown, P. 115830 - 115830

Published: Nov. 1, 2024

Language: Английский

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