Analysis and prediction of industrial energy conservation in underdeveloped regions of China using a data pre-processing grey model

Guo, Hua; Deng, Shengxiang; Yang, Jia; Liu, Jiangwei; Nie, Changda

doi:10.1016/j.enpol.2020.111244

Cited by 17 publications

(5 citation statements)

References 21 publications

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“…In other words, the energy radiated by the sun to the Earth per second is equivalent to 5 million tons of standard coal, which is more than 10,000 times the total energy consumption of the entire world (Pierce, 2016). As an In existing research, various models are used to predict energy consumption, for example, the hybrid forecasting system (Du et al, 2018), computational intelligence technology (Meenal et al, 2018), Granger causality analysis (Pinzón, 2018), NEMS model (Soroush et al, 2017), LEAP model (Dong et al, 2017), time series analysis , LSTM model (Chen et al, 2019), and grey forecasting model (Zeng et al, 2018;Wu et al, 2019;Guo et al, 2020). In all the prediction models, the grey prediction model has attracted substantial attention due to its advantages of convenient use, simple modeling process and high accuracy (Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Forecasting solar energy consumption using a fractional discrete grey model with time power term

Wang

2022

Clean Techn Environ Policy

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Accurate prediction of energy consumption is an important basis for policymakers to formulate and improve energy policies and measures. In this paper, a new grey prediction model FDGM(1,1, t  ) is proposed. The grey wolf optimizer (GWO) is used to optimize the fractional-order r and the time power  in the model. A numerical example and four sets of solar energy consumption data (France, South Korea, OECD, and Asia Pacific region) are used to establish the FDGM(1,1, t  ) model. Based on the idea of metabolism, the solar energy consumption of the above four economies in the next 10 years is predicted. The results show that the FDGM(1,1, t  ) model is more reliable and effective than the other seven grey models. From 2020 to 2029, the solar energy consumption in South Korea, the OECD, and the Asia Pacific region will gradually increase; the solar energy consumption in France will slowly increase in the next few years and will gradually decrease after reaching a peak in 2026. The grey prediction model FDGM(1,1, t  ) proposed in this paper has strong adaptability and can be used not only for the prediction of solar energy consumption but also for the prediction of other energy sources.

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Section: Introductionmentioning

confidence: 99%

Forecasting solar energy consumption using a fractional discrete grey model with time power term

Wang

2022

Clean Techn Environ Policy

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“…Industrial costs-related studies have attracted the attention of many scholars in recent decade and the research topics mainly focused on the cost-benefit analysis in industrial production [2], cost efficiency of different industries [3], carbon dioxide abatement costs of industries [4] and the low cost of industry wastewater treatment [5]. The scholars also studied on power prediction [6] and energy consumption [7] in industrial production, i.e., Ma et al indicated that predicting a price range is practical and desirable [8]. For the construction of cost prediction model, Chakraborty et al developed a new construction cost prediction model using hybrid natural and light gradient boosting [9]; Jiang et al proposed the cost prediction model for products remanufacturing judgment based on backward propagation artificial neural network [10].…”

Section: Introductionmentioning

confidence: 99%

Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction

Wang

Yang

et al. 2022

Int. J. Mach. Learn. & Cyber.

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Industrial cost is a comprehensive indicator to reflect industrial behaviors, whereas industrial data with annotation are rare because the annotation process is very expensive. Data increment transformation is a feasible solution to enrich annotated industrial data, but it bring a new challenge in system modeling because the size of transformational data is the quadratical relationship with that of collected data, and even turn into big data problem. Hence, a novel rule-based system proposed for handling big data problems, called micro-extended belief rule-based system (Micro-EBRBS), is introduced for industrial cost prediction. Firstly, the Micro-EBRBS is improved by 1) the use of activation factor to revise the calculation of individual matching degrees; 2) the use of parameter optimization to determine the optimal value of basic parameters.Afterwards, on the basis of data increment transformation, a novel industrial cost prediction model, called data incrementbased Micro-EBRBS (DIME) model, is developed to accurately predict industrial costs. In case study, 13 state-own holding industries with historical data from 1999 to 2019 in China are used to illustrate the effectiveness of the DIME model.Comparative results show that the DIME model is more accurate than some existing models in industrial cost prediction.

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“…Therefore, it is widely used in many fields (Li et al, 2016). Guo et al used GM(1,1) to study China's industrial energy-saving policy and formulate an ecological development path (Guo et al, 2020). The airline industry is predicted by damp trend GM(1,1) (Carmona-Benítez and Nieto, 2020).…”

Section: Introductionmentioning

confidence: 99%

Modelling the relationship between population density and air quality using fractional Hausdorff grey multivariate model

Shi

2021

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Purpose The proposed model can emphasize the priority of new information and can extract messages from the first pair of original data. The comparison results show that the proposed model can improve the traditional grey model. Design/methodology/approach The grey multivariate model with fractional Hausdorff derivative is firstly put forward to enhance the forecasting accuracy of traditional grey model. Findings The proposed model is used to predict the air quality composite index (AQCI) in ten cities respectively. Originality/value The effect of population density on AQCI in cities with poor air quality is not as significant as that of the cities with better air quality.

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Analysis and prediction of industrial energy conservation in underdeveloped regions of China using a data pre-processing grey model

Cited by 17 publications

References 21 publications

Forecasting solar energy consumption using a fractional discrete grey model with time power term

Forecasting solar energy consumption using a fractional discrete grey model with time power term

Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction

Modelling the relationship between population density and air quality using fractional Hausdorff grey multivariate model

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