Forecasting China's energy intensity by using an improved <i>DVCGM</i> (1, N) model considering the hysteresis effect

Meng, Zhaosu; Xiaotong, Liu; Yin, Kedong; Li, Xuemei; Guo, Xinchang

doi:10.1108/gs-02-2020-0022

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…under the premise of small samples (Meng et al 2021;Liu et al 2021;Ye et al 2021;Luo et al 2020). At this stage, constructing the multivariate grey model still has defects in construction parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

An improved GM(1,1) forecasting model based on Aquila Optimizer for wind power generation in Sichuan Province

2023

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With the rapid development of China's economy, the development of wind resources has important practical significance for alleviating environmental pollution problems in various provinces and cities in China. As China's clean energy province and t western economic center, Sichuan's wind power industry is gradually starting from the 13th Five-Year Plan. Considering the volatility and periodic characteristics of wind power generation in Sichuan Province, this paper proposes an optimized seasonal grey model based on a novel algorithm (Aquila Optimizer). We use dummy variables 1 and 0 to represent seasonal factors and perform seasonal classification of the sample data.According to the classification sequence, we construct a grey prediction model with optimized initial value and background value by Aquila Optimizer. In this paper, we use this model to predict wind power generation in Sichuan Province and verify the validity and rationality of the model by comparing it with other methods. Finally, we further predicted the power generation during the 14th Five-Year Plan period to provide policy advice and planning for the future development of the clean energy industry in Sichuan Province.

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Section: Literature Reviewmentioning

confidence: 99%

An improved GM(1,1) forecasting model based on Aquila Optimizer for wind power generation in Sichuan Province

2023

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“…Wu et al (2019) investigate the multivariate fractional grey GM (α, n) model and apply it to study the effects of China's economic growth and urbanization on energy consumption. For optimizing China's energy structure and EI, Meng et al (2021) construct an improved dummy variables control grey model (DVCGM) and apply grey model (GM) (1, N), DVCGM (1, N) and ARIMA model to test the accuracy of the improved DVCGM (1, N) model prediction.…”

Section: Grey System Theory In Economic Developmentmentioning

confidence: 99%

Grey system theory in sustainable development research—a literature review (2011−2021)

Liu

2022

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PurposeAs the grey systems theory has been widely used in the field of sustainable development (SD) research, in the following, a short literature overview will be put forward, starting from the usage of these theories in the economic development, social inclusion and environmental protection contributions to the evolving process of SD during 2011–2021. The purpose of this paper is to identify some key studies from all the SD areas in which the grey systems can be used in order to open and to bring the researchers to new domains in which they can reveal their interest and in which they can successfully use the methods offered by the grey systems theory.Design/methodology/approachUsing the search engine offered by the Google Scholar and the Web of Science (WoS), a literature review has been performed for the grey systems applications on SD research on both grey relational analysis (GRA) and grey forecasting. In addition, some grey evaluation theories – clustering evaluation models and grey target decision models – have also been presented.FindingsMany grey models are widely used in the field of SD. Compared with other methods such as grey prediction, grey evaluation and decision-making model, GRA technology is the most used method, and the research using this method is more than three times that of all other methods.Research limitations/implicationsThe present paper identifies some of the most representative examples in which the grey system theory (GST) has been used, but, in the same time, there are a lot of studies that have not been mentioned here due to the lack of space.Originality/valueThe present paper focuses on the SD applications in which GST has been successfully used, bringing to the reader a general overview on this field and, in the same time, enables new research perspectives.

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“…Currently, clean energy forecasting is a major hot topic. The recently prevalent prediction models can be classified into four main categories, which are time-series prediction models (Fara et al , 2021; Jamil, 2020), artificial intelligence (Pham et al , 2020; Shafqat et al , 2021; Abdel-Basset et al , 2021), grey prediction models (Qian et al , 2022; Meng et al , 2020; Zhao and Wu, 2020) and hybrid models (Fan et al , 2021; Zhang et al , 2020; Qiao and Yang, 2020). However, time-series prediction models, although simple to model and easy to implement, require a large number of samples to improve the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A novel discrete grey model with time-delayed power term and its application in solar power generation volume forecasting

Bai

Liu

et al. 2022

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PurposeIn order to accurately forecast nonlinear and complex characteristics of solar power generation in China, a novel discrete grey model with time-delayed power term (abbreviated as TDDGM(1,1,tα) is proposed in this paper.Design/methodology/approachFirstly, the time response function is deduced by using mathematical induction, which overcomes the defects of the traditional grey model. Then, the genetic algorithm is employed to determine the optimal nonlinear parameter to improve the flexibility and adaptability of the model. Finally, two real cases of installed solar capacity forecasting are given to verify the proposed model, showing its remarkable superiority over seven existing grey models.FindingsGiven the reliability and superiority of the model, the model TDDGM(1,1,tα) is applied to forecast the development trend of China's solar power generation in the coming years. The results show that the proposed model has higher prediction accuracy than the comparison models.Practical implicationsThis paper provides a scientific and efficient method for forecasting solar power generation in China with nonlinear and complex characteristics. The forecast results can provide data support for government departments to formulate solar industry development policies.Originality/valueThe main contribution of this paper is to propose a novel discrete grey model with time-delayed power term, which can handle nonlinear and complex time series more effectively. In addition, the genetic algorithm is employed to search for optimal parameters, which improves the prediction accuracy of the model.

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Forecasting China's energy intensity by using an improved DVCGM (1, N) model considering the hysteresis effect

Cited by 9 publications

References 20 publications

An improved GM(1,1) forecasting model based on Aquila Optimizer for wind power generation in Sichuan Province

An improved GM(1,1) forecasting model based on Aquila Optimizer for wind power generation in Sichuan Province

Grey system theory in sustainable development research—a literature review (2011−2021)

A novel discrete grey model with time-delayed power term and its application in solar power generation volume forecasting

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