scholarly journals Energy Consumption Predication in China Based on the Modified Fractional Grey Prediction Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jiefang Liu ◽  
Pumei Gao
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Linear Time ◽  
Estimation Method ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Theoretical Support ◽  
Time Varying Parameter ◽  
The Government ◽  
Reasonable Energy

China’s increasing energy consumption poses challenges to economy and environment. How to predict the energy consumption accurately and regulate the future energy consumption production is a problem worth studying. In this paper, the fractional order cumulative linear time-varying parameter discrete grey prediction model (FTDGM (1, 1) model) is introduced. Firstly, the data are preprocessed by buffer operators, and then, the FTDGM (1, 1) model is established. In this paper, the parameter estimation method and the specific process of model establishment are presented. Finally, the models of energy consumption in China are built. The advantages and prediction accuracy of the model established in this paper are analyzed, and the data in the following years are effectively predicted, so as to provide theoretical support for the government to formulate reasonable energy policies.

scholarly journals A Novel Extrapolation-Based Grey Prediction Model for Forecasting China’s Total Electricity Consumption

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin-bo Yang
Keyword(s):  
Prediction Model ◽  
Extreme Values ◽  
Model Performance ◽  
Electricity Consumption ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Background Value ◽  
Development Policies ◽  
The Government

Accurately forecasting China’s total electricity consumption is of great significance for the government in formulating sustainable economic development policies, especially, China as the largest total electricity consumption country in the world. The calculation method of the background value of the GM(1, 1) model is an important factor of unstable model performance. In this paper, an extrapolation method with variable weights was used for calculating the background value to eliminate the influence of the extreme values on the performance of the GM(1, 1) model, and the novel extrapolation-based grey prediction model called NEGM(1, 1) was proposed and optimized. The NEGM(1, 1) model was then used to simulate the total electricity consumption in China and found to outperform other grey models. Finally, the total electricity consumption of China from 2018 to 2025 was forecasted. The results show that China’s total electricity consumption will be expected to increase slightly, but the total is still very large. For this, some corresponding recommendations to ensure the effective supply of electricity in China are suggested.

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

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wuyong Qian ◽  
Hao Zhang ◽  
Aodi Sui ◽  
Yuhong Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Compositional Data ◽  
Development Trend ◽  
Grey Prediction ◽  
Content Type ◽  
Consumption Structure ◽  
Grey Prediction Model ◽  
Proposed Model

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.

scholarly journals Predicting Building Energy Consumption with a New Grey Model

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yan Zhang ◽  
Huiping Wang ◽  
Yi Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Building Energy ◽  
Grey Model ◽  
Annual Growth Rate ◽  
Grey Prediction ◽  
Building Energy Consumption ◽  
Total Energy Consumption ◽  
Grey Prediction Model ◽  
Grey Models

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.

Grey Prediction Model for Water Withdrawals

2013 ◽  
Vol 392 ◽  
pp. 954-957
Author(s):  
Xing Zhao Wu ◽  
Xue Di Wang ◽  
Shu Yi Wei ◽  
Ying Dong
Keyword(s):  
Prediction Model ◽  
The United States ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Main River ◽  
Water Withdrawals ◽  
The Government ◽  
Annual Water ◽  
Few Data ◽  
River Valleys

Since grey prediction model performed well in feasting with few data, we applied it to predicting water withdrawals. We take the prediction of the water withdrawals in the United States as an example. With the data of water withdrawals in the past 30 years, we forecast how much fresh water will be needed in main states of America, which is crucial for the government to make major constructions in national economy. Moreover, we test the model by applying it in the prediction of annual water use in Chinese main river valleys.

scholarly journals Forecasting Solar Energy Consumption Using A Fractional Discrete Grey Model With Time Power Term

2021 ◽  
Author(s):  
Huiping Wang ◽  
Yi Wang
Keyword(s):  
Energy Consumption ◽  
Solar Energy ◽  
South Korea ◽  
Prediction Model ◽  
Asia Pacific ◽  
Grey Wolf Optimizer ◽  
Pacific Region ◽  
Grey Prediction ◽  
Asia Pacific Region ◽  
Grey Prediction Model

Abstract 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, tα ) is proposed. The grey wolf optimizer (GWO) is used to optimize the fractional-order r and the time power α 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, tα ) 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, tα ) 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, tα ) 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.

Grey Prediction of WSN Feedback Value in Greenhouse

2012 ◽  
Vol 608-609 ◽  
pp. 1166-1171
Author(s):  
Wen Feng Cheng ◽  
Xiang Long Yang ◽  
Li Ren Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Monitoring System ◽  
System Stability ◽  
Grey Prediction ◽  
Control Effect ◽  
Current Feedback ◽  
Grey Prediction Model ◽  
Battery Energy ◽  
Model Dimension

To improve the performance of greenhouse monitoring system based on WSN, an algorithm was proposed. By grey prediction of WSN current feedback values, the sampling values of the following variables can be predicted. The paper discussed the relation of grey prediction model dimension and the predicting precision, and the control effect was analyzed. This method solved the discrete and lagging problem, it will effectively save the battery energy of WSN node and reduce the energy consumption in greenhouse by improving system stability.

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