Convolution integral based multivariable grey prediction model for solar energy generation forecasting

2016 ◽  
Author(s):  
Rajendra Narayan Senapati ◽  
Nirod Chandra Sahoo ◽  
Sukumar Mishra
Keyword(s):  
Solar Energy ◽  
Prediction Model ◽  
Energy Generation ◽  
Grey Prediction ◽  
Convolution Integral ◽  
Grey Prediction Model

scholarly journals Nonlinear Grey Prediction Model with Convolution Integral NGMC(1,n)and Its Application to the Forecasting of China’s Industrial SO2Emissions

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zheng-Xin Wang
Keyword(s):  
Prediction Model ◽  
Least Squares Method ◽  
Influencing Factor ◽  
Grey Model ◽  
Grey Prediction ◽  
Convolution Integral ◽  
Grey Prediction Model ◽  
Economic Output ◽  
Decision Variables ◽  
Improved Accuracy

The grey prediction model with convolution integral GMC (1,n) is a multiple grey model with exact solutions. To further improve prediction accuracy and describe better the relationship between cause and effect, we introduce nonlinear parameters into GMC (1,n) model and additionally apply a convolution integral to produce an improved forecasting model here designated as NGMC (1,n). The model solving process applied the least-squares method to evaluate the structure parameters of the model: convolution was used to obtain an exact solution with this improved grey model. The nonlinear optimisation took the parameters as the decision variables with the objective of minimising forecasting errors. The GMC (1, 2) and NGMC (1, 2) models were used to predict China’s industrial SO2emissions from the basis of the economic output level as the influencing factor. Results indicated that NGMC (1, 2) can effectively describe the nonlinear relationship between China’s economic output and SO2emissions with an improved accuracy over current GMC (1, 2) models.

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.

Dynamic Analysis and Prediction of Ecological Footprint in Jilin Province of China Based on Grey Prediction Model

2014 ◽  
Vol 472 ◽  
pp. 899-903 ◽  
Author(s):  
Biao Gao ◽  
Qing Tao Xu
Keyword(s):  
Prediction Model ◽  
Ecological Footprint ◽  
Jilin Province ◽  
Grey Prediction ◽  
Dynamic Changes ◽  
Ecological Capacity ◽  
Grey Prediction Model ◽  
Ecological Deficit ◽  
Gray Prediction ◽  
Per Capita

The paper calculates ecological footprint per capita and ecological capacity per capita in the Jilin province during 1998 and 2010 by using the ecological footprint theory, and analyzes the dynamic changes of ecological footprint per capita and ecological capacity per capita, and obtains development prediction model of ecological footprint per capita and ecological capacity per capita based on grey prediction model. The results indicate the ecological footprint per capita had increased continuously from 1.7841 hm2 per capita to 3.2013 hm2 per capita between 1998 and 2010. During this period, ecological capacity per capita dropped from 1.3535 hm2 per capita to 1.3028 hm2 per capita. Ecological deficit had increased from 0.4306 hm2 per capita to 1.8985 hm2 per capita that showed that the development of Jilin province was in an unsustainable status. The gray prediction model shows the ecological footprint per capita in the Jilin province will increase from 3.4833 hm2 per capita to 5.7022 hm2 per capita between 2011 and 2020, ecological capacity per capita will drop from 1.2978 hm2 per capita to 1.2676 hm2 per capita and ecological deficit will increase from 2.1855 hm2 per capita to 4.4346 hm2 per capita.

scholarly journals Forecasting the Number of New Coronavirus Infections Us-ing an Improved Grey Prediction Model

2021 ◽  
Author(s):  
Hui Li ◽  
Bo Zeng ◽  
Jianzhou Wang ◽  
Hua’an Wu
Keyword(s):  
Prediction Model ◽  
Particle Swarm Optimization Algorithm ◽  
Percentage Error ◽  
Small Data ◽  
Grey Prediction ◽  
Matlab Simulation ◽  
Short Term ◽  
New Model ◽  
Grey Prediction Model ◽  
Coronavirus Infections

Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. Methods: The number of new coronavirus infections was characterized by “small data, poor information” in the short term. The grey prediction model provides an effective method to study the prediction problem of “small data, poor information”. Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts for-ward specific suggestions.

Research on Method of Metro Monitoring Model Based on Grey Prediction Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 641-645
Author(s):  
Mao Hua Liu ◽  
Xiu Bo Sun
Keyword(s):  
Prediction Model ◽  
Safety Monitoring ◽  
Prediction Algorithm ◽  
Grey Prediction ◽  
Monitoring Point ◽  
Value Analysis ◽  
Grey Prediction Model ◽  
Monitoring Model ◽  
Surface Monitoring ◽  
Set Up

Grey prediction model is a model to predict the trend maturely, its application in the subway safety monitoring is of great significance. Set up by MATLAB software to complete the grey prediction model, and take the surface monitoring point for example, Comparing the prediction value with the actual measured value, analysis by the accuracy, obtain the trend of surface change around the subway station.

Forecasting China's electricity consumption using a new grey prediction model

Energy ◽  
2018 ◽  
Vol 149 ◽  
pp. 314-328 ◽  
Author(s):  
Song Ding ◽  
Keith W. Hipel ◽  
Yao-guo Dang
Keyword(s):  
Prediction Model ◽  
Electricity Consumption ◽  
Grey Prediction ◽  
Grey Prediction Model
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