A novel self-adaptive fractional multivariable grey model and its application in forecasting energy production and conversion of China

Wang, Yong; Wang, Li; Ye, Lingling; Ma, Xin; Wu, Wenqing; Yang, Zhongsen; He, Xinbo; Zhang, Lei; Zhang, Yuyang; Zhou, Ying; Luo, Yongxian

doi:10.1016/j.engappai.2022.105319

Cited by 24 publications

(8 citation statements)

References 31 publications

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“…On the other hand, regarding optimization problems, evolutionary algorithms with swarm intelligence methods are applied in many fields, from biology [20], chemistry [21,22], energy production [23][24][25], neural networks training and design [26][27][28], to humanities [29]. This field was revolutionized by the development of two families of methods in 1995: Differential Evolution (DE) [30] and Particle Swarm Optimization (PSO) [31].…”

Section: Related Workmentioning

confidence: 99%

Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization

Birdal

2024

Environ. Res. Commun.

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Electricity consumption is expected to increase considerably in the next few years, so forecasting and planning will become more important. A new method of forecasting electricity loads based on air pollution is presented in this paper. Air pollution indirect effects are not incorporated in current evaluations since they rely primarily on weather conditions, historical load data, and seasonality. The accuracy of electricity load forecasting improved by incorporating air pollution data and its potential effects, especially in regions where air quality heavily impacts energy consumption and generation patterns. This robust prediction model is capable of capturing the complex interactions between air pollution and electricity load by integrating innovative environmental factors with historical load data, weather forecasts, and other features. As part of the second contribution, we use metaheuristic algorithms to optimize hyper parameters, which provide advantages such as exploration capability, global optimization, robustness, parallelization, and adaptability making them valuable tools to improve machine learning models’ performance and efficiency. The study found that the correlation coefficient (R) between predicted and real electricity demand and supply was high, at 0.9911. Beyond that this approach reduces MAPE by up to 19.5% when CNN and particle swarm optimization are combined with utilizing innovative air pollution variables. As a result, the optimization results were robust compared to state-of-the-art results based on statistical metrics such as RMSE and MAPE. Lastly, we emphasize the importance of factoring in air pollution effects when forecasting and managing electricity loads; future research directions include developing integrated modeling frameworks that reflect the dynamic interaction between air quality, energy consumption, and renewable energy generation.

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Section: Related Workmentioning

confidence: 99%

Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization

Birdal

2024

Environ. Res. Commun.

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“…Improving the forecasting accuracy of the FGM(1,1) model is a primary research focus that entails proposing various fractional-order forms [38][39][40], constructing prediction models with different fractional-order structures [41][42][43][44], investigating the optimal number of modeling samples [17,45], and integrating other optimization algorithms to determine the optimal fractional order of the model [28,39,46], among other approaches. Although existing research has conducted in-depth discussions on the fractional-order grey prediction model, almost all of them have used a fixed fractional-order value.…”

Section: Advancements In Fractional-order Grey Prediction Modelsmentioning

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

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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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“…Data were obtained from the reference (Yong Wang et al 2022) were used for simulation and data from 2012 to 2013 were used for validation. To demonstrate the validity of the UNGMC(1,3) model, five excellent grey models were used for comparative analysis.…”

Section: Case2 Forecasting Total Per Capita Energy Consumption In Chinamentioning

confidence: 99%

An unequal new information priority grey forecasting air pollution urban model considering the regional socio-economic development

Dang

Wang

2023

Preprint

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The prediction of air quality has been a hot research topic in environmental issues. Due to the obvious regional characteristics of air pollution in Handan, the prediction of the Air Quality Composite Index (AQCI) in Handan was carried out considering the socio-economic factors in Handan and the surrounding cities. Firstly, the main factors affecting the AQCI of Handan were screened from the added value of primary industry, added value of industry, added value of construction industry, added value of tertiary industry, number of resident population and vehicle ownership in Handan and its five surrounding cities using a grey correlation analysis model. Then, considering the variability of development trends and information priorities among different city factors, a grey unequal new information priority multivariate convolution prediction model (UNGMC(1,N)) is proposed, and the particle swarm algorithm is used to adaptively optimize the nonlinear model parameters. The model comparison results show that the stability and prediction accuracy of the proposed UNGMC(1,N) model are significantly higher than those of other grey models and some popular air quality prediction methods. According to the research results, the air quality of Handan city is mainly influenced by the number of permanent residents in Handan city, and the air quality of Handan city is negatively correlated with the number of permanent residents in Handan city. In terms of regional pollution, the air quality of Handan city shows a positive correlation with the added value of tertiary industry in the neighboring city of Anyang. In addition, Handan's air quality will continue to improve in the future with U-shaped improvement efficiency and 2024 will be a critical year for Handan's air quality improvement efficiency.

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A novel self-adaptive fractional multivariable grey model and its application in forecasting energy production and conversion of China

Cited by 24 publications

References 31 publications

Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization

Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization

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

An unequal new information priority grey forecasting air pollution urban model considering the regional socio-economic development

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