A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

PurposeThe purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.Design/methodology/approachDue to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.FindingsChina's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.Originality/valueThe paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

Research on the cost effectiveness of carbon emission reduction in the full life cycle of electric vehicles based on grey prediction

In order to research the carbon emission reduction potential of electric vehicles, a cost effectiveness model is used to calculate and compare the economic costs and carbon emissions of fuel vehicles and electric vehicles throughout the life cycle, and an improved grey prediction model is utilized to analyze the future trends of electric vehicle emission reduction benefits. The results show that electric vehicles play a positive role in carbon emission reduction, and the unit cost of carbon emission reduction is decreasing by years. Therefore, China should vigorously develop the electric vehicle industry and technology, and achieve the strategic goal of carbon emission reduction by promoting the electrification of vehicles.

Prediction of Income of Rural Residents Based on Markov Model under the Background of Rural Revitalization

The income of rural residents is not only an important indicator to measure the development of rural economy, but also a key factor in the livelihood of farmers. The accurate prediction of income of rural residents can provide data supporting for promoting rural revitalization strategy. This paper selects per capita disposable income of rural residents in China as the research object and uses the macrostatistical data from 2011 to 2020 to predict farmers’ income. Firstly, the grey prediction model is constructed, and the grey prediction value is corrected by Markov chain. The simulation value is compared with the real value. And the results show that the prediction accuracy of the model is higher. It shows that the results of using the grey Markov model to predict the income of rural residents in China from 2021 to 2025 are reliable. Finally, the article puts forward policy recommendations to promote the income of rural residents.

Application of non-equidistant GM(1,1) model based on the fractional-order accumulation in building settlement monitoring

Non-equidistant GM(1,1) (abbreviated as NEGM) model is widely used in building settlement prediction because of its high accuracy and outstanding adaptability. To improve the building settlement prediction accuracy of the NEGM model, the fractional-order non-equidistant GM(1,1) model (abbreviated as FNEGM) is established in this study. In the modeling process of the FNEGM model, the fractional-order accumulated generating sequence is extended based on the first-order accumulated generating sequence, and the optimal parameters that increase the prediction precision of the model are obtained by using the whale optimization algorithm. The FNEGM model and the other two grey prediction models are applied to three cases, and five prediction performance indexes are used to evaluate the prediction precision of the three models. The results show that the FNEGM model is more suitable for predicting the settlement of buildings than the other two grey prediction models.

Research on Performance Prediction of Highway Asphalt Pavement Based on Grey–Markov Model

Reasonable and accurate forecasts can be used by the highway maintenance management department to determine the best maintenance timing and strategy, which can keep the highway performing well and maximize its social and economic benefits. A Grey–Markov combination model is established in this paper to predict highway pavement performance accurately based on the Grey GM (1, 1) model (a single-variable Grey prediction model with a first-order difference equation) and revised by the Markov model. The advantages of the short-term forecast Grey model and the probabilistic Markov model, which considers the fate of pavement performance prediction, are comprehensively applied to the combined forecasting model. The Grey GM (1, 1), Grey–Markov model and Liu-Yao model are adopted to predict the pavement condition index (PCI) based on the actual PCI values measured in Shanxi, Chongqing, and Shaoguan. The average relative errors of the above three models’ predicted values in Shanxi are 0.73%, 1.18%, and 0.67%, respectively, from 2012 to 2014. Thus, the prediction errors of the three models are relatively close. The average relative errors of the prediction values predicted by the three models are 3.89%, 0.67%, and 0.50%, respectively, from 2015 to 2019. The latter two errors are more minor than the Grey GM (1, 1) model. Two other regions have similar conclusions. The results show that the prediction accuracy of the combination Grey–Markov prediction model established in this paper is feasible to predict asphalt pavement performance in China.

Predicting Building Energy Consumption with a New Grey Model

Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, t α )), introduces the modeling mechanism and characteristics of the FDGM (1, 1, t α ), and uses three groups of data to verify its effectiveness compared with that of other grey models. This paper forecasts the building energy consumption in China over the next five years based on the idea of metabolism. The results show that the FDGM (1, 1, t α ) can be transformed into other grey models through parameter setting changes, so the new model has strong adaptability. The FDGM (1, 1, t α ) is more reliable and effective than the other six compared grey models. From 2018 to 2022, the total energy consumption levels of civil buildings, urban civil buildings, and civil buildings specifically in Beijing will exhibit steady upward trends, with an average annual growth rate of 2.61%, 1.92%, and 0.78%, respectively.