Application of Grey Prediction Model in Electric Power Consumption in Shandong Province

2012 ◽  
Vol 524-527 ◽  
pp. 3021-3026
Author(s):  
Ke Ying Zhang ◽  
Jing Zhao ◽  
Yang Dong Li
Keyword(s):  
Power Consumption ◽  
Prediction Model ◽  
Electric Power ◽  
Forecast Accuracy ◽  
Reference Value ◽  
Shandong Province ◽  
Electric Power Consumption ◽  
Grey Prediction ◽  
Grey System ◽  
Grey Prediction Model

This paper takes Shandong Province as an example, by using grey system forecast theory; the GM (1, 1) model of electric power consumption is established. The electric power consumption from 2011 to 2015 in Shandong Province is forecasted and the forecast accuracy is verified, which offers the reference value for Shandong Province electric power planning in the future.

scholarly journals Application of a Fractional Grey Prediction Model Based on a Filtering Algorithm in Image Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyu Tong ◽  
Kailiang Shao ◽  
Xilin Luo ◽  
Huiming Duan
Keyword(s):  
Image Processing ◽  
Prediction Model ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Middle Point ◽  
Filtering Algorithm ◽  
Grey Correlation ◽  
Grey Prediction Model ◽  
Model Based

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

Analysis of the Characteristics and Effects for Sichuan Province Electric Power Consumption

2014 ◽  
Vol 672-674 ◽  
pp. 1387-1392
Author(s):  
Ce Chen ◽  
Xing Qi He
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Electric Power ◽  
Reference Value ◽  
Power Market ◽  
Sichuan Province ◽  
Electric Power Consumption ◽  
Electric Power Market ◽  
Consumption Energy ◽  
Using Data

Using data from 2007 to 2011 as the sample, on the base of analyzing the power consumption overall situation of Sichuan Province, the effects of Sichuan electric consumption, energy consumption characteristics factors, increase the proportion of the reasons were studied. The research results to improve the operation quality and efficiency of management, further develop the electric power market has a certain reference value.

Study on Power Load Forecasting Base on Grey Prediction Model by Slip Average Method

2011 ◽  
Vol 84-85 ◽  
pp. 752-756
Author(s):  
Zheng Yuan Jia ◽  
Zhi Wei Huang ◽  
Chun Mei Wang ◽  
Gang Zhang
Keyword(s):  
Prediction Model ◽  
Electric Power ◽  
Average Method ◽  
Original Data ◽  
Power Demand ◽  
Grey Prediction ◽  
Factors Affecting ◽  
Grey Prediction Model ◽  
Power Load ◽  
Set Up

The grey control theory is used to predict electric power demand in this paper. Original data is processed by the Generation Method. Many unimportant factors affecting electric power demand are removed,and useful information is extracted from original data. The differential fitting equation is set up,and grey prediction model modified by slip average method is presented with residual modification. The current year data is possessed with high weight,which avoids excessive fluctuation. Predicting results show that the model is effective to improve the predict precision.

scholarly journals Multivariate Grey Prediction Model with Priority Accumulation of New Information

2021 ◽  
Vol 248 ◽  
pp. 02009
Author(s):  
Lanxi Zhang
Keyword(s):  
Prediction Model ◽  
Absolute Error ◽  
Good Effect ◽  
Grey Prediction ◽  
Grey System ◽  
Engineering Project ◽  
Average Percentage ◽  
Grey Prediction Model ◽  
New Information ◽  
Proposed Model

According to the new information priority principle of grey system, this paper tries to optimize the traditional multivariate grey prediction model. Firstly, the basic theory of the traditional grey prediction model is put forward. Based on this, the background value is improved by using the new information priority principle, and the cumulative generation with parameters is defined. Taking the settlement trend of A4# building of an engineering project in Anhui province as an example, the model is applied to the settlement analysis, and the proposed model is compared with the existing grey prediction model, the average percentage absolute error between the predicted value and the observed value is calculated, and the regression graphs of each model are drawn. Through the analysis, we can see that the established model has achieved a good effect, and then verified the practicability and reliability of the proposed model.

scholarly journals Forecasting Oil Consumption with Novel Fractional Grey Prediction Model Based on Simpson Formula

2019 ◽  
pp. 1-27
Author(s):  
Xiwang Xiang ◽  
Peng Zhang ◽  
Lang Yu
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Model Error ◽  
Grey System Theory ◽  
Human Society ◽  
Grey Prediction ◽  
Oil Consumption ◽  
Grey System ◽  
Grey Prediction Model ◽  
Grey Models

With the development of human society, the evolving transition of energy will become a common challenge that mankind has to face together. In this context, it is crucial to make scientific and reasonable predictions about energy consumption. This paper presents a novel fractional grey prediction model FGM(1,1,k2) based on the classical fractional grey system theory. In order to improve the prediction accuracy of the FGM(1,1,k2) model, we further analyze the model error and propose improved grey model called as SFGM with optimization of background value. The numerical cases point out that SFGM(1,1,k2) significantly outperforms other existing fractional grey models. Finally, the proposed SFGM(1,1,k2) is applied to the forecasting of oil consumption, the predicted results would provide a reference for making energy policy in new situations.

Software including the functions of automated analysis of electric power consumption in pipeline oil transportation

2011 ◽  
Vol 8 (1) ◽  
pp. 233-238
Author(s):  
R.M. Bogdanov ◽  
S.V. Lukin
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Data Model ◽  
Automated Analysis ◽  
Petroleum Products ◽  
Electric Power Consumption ◽  
Scientific Center ◽  
Relational Data Model ◽  
Oil Transportation ◽  
Academy Of Sciences

Oil and petroleum products transportation is characterized by a significant cost of electric power. Correct oil and petroleum products accounting and forecasting requires knowledge of many factors. The software for norms of electric power consumption analysis for the planned period was developed at the Ufa Scientific Center of the Russian Academy of Sciences. Based on the principles of the relational data model, a schematic diagram/arrangement for the main oil transportation objects was developed, which allows to hold the initial data and calculated parameters in a structured manner.

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.

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