A new grey model with generalized fractal-fractional derivative for prediction of tourism development DOI Creative Commons
Chenhui Xu,

Jianguo Zheng

Deleted Journal, Journal Year: 2024, Volume and Issue: 7(1)

Published: Dec. 27, 2024

Abstract A new fractional order grey prediction model is proposed for accurate forecasting of tourism development in China. The combines generalized fractal-fractional derivative operators with difference and accumulation generation operators. Experimental comparisons existing models show significant improvements accuracy efficiency. applied to forecast China results are compared actual data verify effectiveness. improve efficiency, accounting various factors affecting development. Comparisons superiority accurately predicts China, resulting improved methods. Comparison further validates the by displaying agreement between predicted values. Overall, effectively captures dynamics forecasting.

Language: Английский

Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics DOI
Junjie Wang,

Wenyu Huang,

Yuanping Ding

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134518 - 134518

Published: Jan. 1, 2025

Language: Английский

Citations

2
A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation DOI
Junjie Wang, Li Ye, Xiaoyu Ding

et al.

Energy, Journal Year: 2024, Volume and Issue: 304, P. 131939 - 131939

Published: June 4, 2024

Language: Английский

Citations

10
Research on Cross-Border e-Commerce Supply Chain Prediction and Optimization Model Based on Convolutional Neural Network Algorithm DOI Creative Commons

Yajie Zhao,

Bin Gong, Bo Huang

et al.

Journal of Advanced Computational Intelligence and Intelligent Informatics, Journal Year: 2025, Volume and Issue: 29(1), P. 215 - 223

Published: Jan. 19, 2025

Enhancing the precision of supply chain management and reducing operational costs are crucial for development cross-border e-commerce market. However, existing research often overlooks demand uncertainty caused by seasonal variations challenges handling returns in logistics. Therefore, this paper proposes a SARIMA-CNN-BiLSTM prediction model that effectively captures both nonlinear characteristics chains. Additionally, incorporating process, distribution optimization is developed with objective minimizing total costs. The solved using an improved whale algorithm. In validation real-world data, achieved mean absolute percentage error reduction 6.479 7.703 compared to convolutional neural network (CNN) BiLSTM models, respectively. Moreover, chosen algorithm reduced cost 231,310 CNY, 62,564 131,632 CNY algorithm, genetic particle swarm optimization, proposed approach provides robust support enterprises enhancing efficiency their operations.

Language: Английский

Citations

1
A novel conformable fractional logistic grey model and its application to natural gas and electricity consumption in China DOI
Hui Li, Huiming Duan, Yuxin Song

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122591 - 122591

Published: Feb. 1, 2025

Language: Английский

Citations

1
A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting DOI
Ligang Cui,

Yingcong Chen,

Jie Deng

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 254, P. 124409 - 124409

Published: June 7, 2024

Language: Английский

Citations

7
A novel grey seasonal model with time power for energy prediction DOI
Weijie Zhou, Jiaxin Chang, H Jiang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 259, P. 125356 - 125356

Published: Sept. 10, 2024

Language: Английский

Citations

6
Time-varying polynomial grey prediction modeling with integral matching DOI
Lili Ye, Naiming Xie, John E. Boylan

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 290, P. 111581 - 111581

Published: Feb. 28, 2024

Language: Английский

Citations

4
A new information priority grey prediction model for forecasting wind electricity generation with targeted regional hierarchy DOI

Xupeng Guo,

Yaoguo Dang,

Song Ding

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 252, P. 124199 - 124199

Published: May 10, 2024

Language: Английский

Citations

4
The development trend of China’s marine economy: a predictive analysis based on industry level DOI Creative Commons
Yu Chen,

Huahan Zhang,

Lingling Pei

et al.

Frontiers in Marine Science, Journal Year: 2025, Volume and Issue: 12

Published: Feb. 10, 2025

This paper aims to provide insights into the future trends for marine industries in China, by forecasting added value key sectors and then offering tailored policy recommendations. Those economic indicators at industry level are characterized small sample sizes, sectoral heterogeneity, irregular fluctuations, which require a specialized methodology handle data features predictions each industry. To address these issues, conformable fractional grey model ( CFGM ), integrates accumulation with model, is applied proven effective through accuracy robustness tests. First, results from multi-step experiments demonstrate that significantly outperforms traditional statistical, machine learning models, models context of predictions, an average improvement 32.14%. Second, stability predictive values generated further verified Probability Density Analysis PDA ) multiple comparisons best MCB tests, thereby ruling out possibility accurate result mere chance. Third, used estimate across industries, accompanied suggestions ensure sustainable development economy.

Language: Английский

Citations

0
Research on the technical framework and critical path of new energy portfolio prediction based on multi-algorithm fusion DOI Open Access

Zhongyuan Yan,

Wenbo Xue, Yi Zhang

et al.

Applied Mathematics and Nonlinear Sciences, Journal Year: 2025, Volume and Issue: 10(1)

Published: Jan. 1, 2025

Abstract In recent years, the new energy power prediction technology has been developing continuously, but there is still problem of energy's relatively fragile ability to tolerate extreme weather in actual operation. Therefore, this paper proposes a combination model based on ordered weighted average operator improve accuracy under complex for operation and production needs dispatch. According basic process wind solar prediction, Shapley value method utilized calculate weights. The Logistic model, time series ARMA gray GM (1, 1) are used as single models constituting combined induced IOWA introduced establish by assigning high low ranking fitting methods. Aiming at seasonal daily characteristics PV power, influence different types error investigated. Comparison carried out analyze effect proposed paper. maintains level conditions. overall fluctuation range its absolute ultra-short-term kept within 0~3. fusion multiple algorithms designed can multi-dimensional scenarios, provide support dispatch energy-based systems market environments.

Language: Английский

Citations

0