Application of a novel hybrid accumulation grey model to forecast total energy consumption of Southwest Provinces in China

Zhao, None X.; Ma, Xin; Cai, Yubin; Yuan, Hong; Deng, Yanqiao

doi:10.1108/gs-02-2023-0013

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…Grey prediction models achieve nonlinear mapping using a limited number of samples without requiring the data to possess any statistical assumptions [26]. Therefore, grey prediction models are widely used in energy, transportation, water conservancy, economy, tourism, and population [29][30][31][32]. The grey prediction model stands out from other prediction models due to its unique approach of utilizing data modeling through the accumulative generation operation (AGO), rather than directly estimating and modeling the original data [33].…”

Section: Grey Models 131 Basic Principles Of Grey Forecasting Modelsmentioning

confidence: 99%

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Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

Wu,

Fu,

Jiang

et al. 2024

Fractal Fract

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International students play a crucial role in China’s talent development strategy. Thus, predicting overseas talent mobility is essential for formulating scientifically reasonable talent introduction policies, optimizing talent cultivation systems, and fostering international talent cooperation. In this study, we proposed a novel fractional-order grey model based on the Multi-Layer Perceptron (MLP) and Grey Wolf Optimizer (GWO) algorithm to forecast the movement of overseas talent, namely MGDFGM(1,1). Compared to the traditional grey model FGM(1,1), which utilizes the same fractional order at all time points, the proposed MGDFGM(1,1) model dynamically adjusts the fractional-order values based on the time point. This dynamic adjustment enables our model to better capture the changing trends in the data, thereby enhancing the model’s fitting capability. To validate the effectiveness of the MGDFGM(1,1) model, we primarily utilize Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as the evaluation criteria for the prediction accuracy, as well as standard deviation (STD) as an indicator of the model stability. Furthermore, we perform experimental analysis to evaluate the predictive performance of the MGDFGM(1,1) model in comparison to NAÏVE, ARIMA, GM(1,1), FGM(1,1), LSSVR, MLP, and LSTM. The research findings demonstrate that the MGDFGM(1,1) model achieves a remarkably high level of prediction accuracy and stability for forecasting overseas talent mobility in China. The implications of this study offer valuable insights and assistance to government departments involved in overseas talent management.

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Section: Grey Models 131 Basic Principles Of Grey Forecasting Modelsmentioning

confidence: 99%

“…According to the MAPE accuracy criterion [60], a model is deemed to have a high prediction accuracy if the error is below 10%, indicating its suitability for prediction, as shown in Table 1. This study applies STD as a measure to test the stability of the models utilized in this paper [30] as follows:…”

Section: Model Evaluation Criteriamentioning

confidence: 99%

Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

Wu,

Fu,

Jiang

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

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“…It has been more than 40 years since grey system theory was first proposed by Professor Deng in 1982 (Deng, 1982), and it is now widely used in many fields such as agriculture, industry, water conservancy, economy and energy (Luo and Li, 2023;Tulkinov, 2023;Zhao et al, 2023;Hu, 2023). The Grey Forecasting Theory, as the core system of grey system theory, has achieved a series of important findings after years of research and development (Zhang et al, 2023;Sapnken, 2023;Li et al, 2023a;Wei et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Damping accumulated discrete MGM(1, m) power model and its application to forecasting agricultural output value share and employment share

Li,

Luo

2024

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PurposeThe damping accumulated discrete MGM(1, m) power model is proposed for the problem of forecasting the share of agricultural output value and the share of employment in China.Design/methodology/approachIn this study, the damping accumulated discrete MGM(1, m) power model was developed based on the idea of discrete modelling by introducing a damping accumulated generating operator and power index. The new model can better identify the non-linear characteristics existing between different factors in the multivariate system and can accurately describe and forecast the trend of changes between data series and each of them.FindingsThe validity and rationality of the new model are verified through numerical experiment. It is forecasted that in 2023, the share of agricultural output value in China will be 7.14% and the share of agricultural employment will be 21.98%, with an overall decreasing trend.Practical implicationsThe simultaneous decline in the share of agricultural output value and the share of employment is a common feature of countries that have achieved agricultural modernisation. Accurate forecasts of the share of agricultural output value and the share of employment can provide an important scientific basis for formulating appropriate agricultural development targets and policies in China.Originality/valueThe new model proposed in this study fully considers the importance of new information and has higher stability. The differential evolutionary algorithm was used to optimise the model parameters.

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“…Deng (1989) invented the grey forecasting model to simulate non-homogeneous exponential time sequences. After that, many extended nonlinear grey forecasting models (Xie, 2022) are developed to describe the nonlinearity, periodicity and non-monotonic behaviors of time series, such as grey Verhulst model (Zeng et al ., 2020), grey Riccati model (Xiao and Duan, 2020), grey models with fractional order accumulation (Wu et al ., 2013; Zhao et al ., 2023), grey model with time power term (Li et al ., 2023), grey polynomial model (Luo and Wei, 2017) and discrete grey models (Xie and Liu, 2008; Liu et al ., 2021; Qian et al ., 2022). For seasonal fluctuation sequences, researchers have conducted extensive studies in three aspects, i.e.…”

Section: Introductionmentioning

confidence: 99%

A novel time-varying grey Fourier model for variable amplitude seasonal fluctuation sequences

Liu,

Ma,

Gao

et al. 2024

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PurposeA time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.Design/methodology/approachThe proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.FindingsThe new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPEF and MAPET are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).Originality/valueThe parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

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Application of a novel hybrid accumulation grey model to forecast total energy consumption of Southwest Provinces in China

Cited by 11 publications

References 30 publications

Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

Damping accumulated discrete MGM(1, m) power model and its application to forecasting agricultural output value share and employment share

A novel time-varying grey Fourier model for variable amplitude seasonal fluctuation sequences

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