Research on Mid-Long Term Load Forecasting Based on Gray Fourier Series Residual Correction Model

2014 ◽  
Vol 631-632 ◽  
pp. 345-349
Author(s):  
Dong Xiao Niu ◽  
Peng Wang ◽  
Qiong Wang ◽  
Bing Yi Liu ◽  
Wei Chang Zhang ◽  
...  
Keyword(s):  
Fourier Series ◽  
Prediction Model ◽  
Moving Average ◽  
Load Forecasting ◽  
Model Fitting ◽  
Original Data ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Power Load ◽  
Residual Correction

The purpose of power load forecasting is to provide the development status and level of the region's future load, providing the basis for the electric power production department and management department to make production and development plans. This paper puts forward the grey prediction model modified by Fourier series residual. First of all, the moving average method is used to preprocess the original data and weaken the burr of the original data. Secondly, the GM (1, 1) is used to estimate the load of the selected sample area. On this basis, use the Fourier series to revise the residual error of the grey forecasting model, making the model fitting of historical data as much as possible. The example analysis results show that the grey prediction model modified by Fourier series residual has the higher prediction accuracy compared with the general GM (1,1), proving the validity of the model.

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.

GM (1,1) Model Application in Power Load Forecasting Based on Sliding Average Method

2014 ◽  
Vol 687-691 ◽  
pp. 1300-1303
Author(s):  
Li Zhi Song
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Load Forecasting ◽  
Prediction Method ◽  
Grey Model ◽  
Grey Prediction ◽  
Model Application ◽  
Dispersion Degree ◽  
Power Load ◽  
Power Load Forecasting

The grey prediction method is simple in principle, the sample size was small and simple, suitable for load forecasting.But grey model has some limitations, the data dispersion degree is more bigger,the gray is also more bigger, it will reduce the accuracy of prediction.This paper adopts the moving average method to improve the raw data , so as to increase the data weights, while avoiding predicted value excessive volatility .Through a city of China's power load is instantiated to verify, and Then analyze the results, found that after the GM (1,1) model improved by moving average method can effectively improve the accuracy of load forecasting.

A multiperiod grey prediction model and its application

2021 ◽  
pp. 1-10
Author(s):  
D. Luo ◽  
G.Z. Zhang
Keyword(s):  
Fourier Series ◽  
Prediction Model ◽  
Mean Square Error ◽  
Loss Rate ◽  
Minimum Mean Square Error ◽  
The Novel ◽  
Mean Square ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Novel Model

The purpose of this paper is to solve the prediction problem of nonlinear sequences with multiperiodic features, and a multiperiod grey prediction model based on grey theory and Fourier series is established. For nonlinear sequences with both trend and periodic features, the empirical mode decomposition method is used to decompose the sequences into several periodic terms and a trend term; then, a grey model is used to fit the trend term, and the Fourier series method is used to fit the periodic terms. Finally, the optimization parameters of the model are solved with the objective of obtaining a minimum mean square error. The novel model is applied to research on the loss rate of agricultural droughts in Henan Province. The average absolute error and root mean square error of the empirical analysis are 0.3960 and 0.5086, respectively. The predicted results show that the novel model can effectively fit the loss rate sequence. Compared with other models, the novel model has higher prediction accuracy and is suitable for the prediction of multiperiod sequences.

Analyzing China’s OFDI using a novel multivariate grey prediction model with Fourier series

2019 ◽  
Vol 12 (3) ◽  
pp. 352-371 ◽  
Author(s):  
Hang Jiang ◽  
Yi-Chung Hu ◽  
Jan-Yan Lin ◽  
Peng Jiang
Keyword(s):  
Fourier Series ◽  
Prediction Model ◽  
Technological Development ◽  
Spillover Effect ◽  
International Investment ◽  
Chinese Government ◽  
Investment Strategies ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model

Purpose With the development of economy, China’s OFDI constantly increase in recent year. Meanwhile, OFDI has spillover effect on economic development and technological development of home country. Thus, accurate OFDI prediction is a prerequisite for the effective development of international investment strategies. The purpose of this paper is to predict China’s OFDI accurately using a novel multivariable grey prediction model with Fourier series. Design/methodology/approach This paper applied a multivariable grey prediction model, GM(1,N), to forecast China’s OFDI. In order to improve the prediction accuracy and without changing local characteristics of grey model prediction, this paper proposed a novel grey prediction model to improve the performance of the traditional GM(1,N) model by combining with residual modification model using GM(1,1) model and Fourier series. Findings The coefficients indicate that the export and GDP have positive influence on China’s OFDI, and, according to the prediction result, China’s OFDI shows a growing trend in next five years. Originality/value This paper proposed an effective multivariable grey prediction model that combined the traditional GM(1,N) model with a residual modification model in order to predict China’s OFDI. Accurate forecasting of OFDI provides reference for the Chinese Government to implement international investment strategies.

scholarly journals A Nonhomogeneous Multivariable Grey Prediction NMGM Modeling Mechanism and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Haixia Wang ◽  
Lingdi Zhao
Keyword(s):  
Prediction Model ◽  
Least Square Method ◽  
Original Data ◽  
Least Square ◽  
The Novel ◽  
Grey Prediction ◽  
Prediction Theory ◽  
Grey Prediction Model ◽  
Index Data ◽  
Proposed Model

The purpose of this paper is to explore modeling mechanism of a nonhomogeneous multivariable grey prediction NMGM(1, m, kα) model and its application. Although multi-variable grey prediction MGM(1, m) model has been employed in many fields, its prediction results are not always satisfactory. Traditional MGM(1, m) model is constructed on the hypothesis that original data sequences are in accord with homogeneous index trend; however, the nonhomogeneous index data sequences are the most common data existing in all systems, and how to handle multivariable nonhomogeneous index data sequences is an urgent problem. This paper proposes a novel nonhomogeneous multivariable grey prediction model termed NMGM(1, m, kα) to deal with those data sequences that are not in accord with homogeneous index trend. Based on grey prediction theory, by least square method and solutions of differential equations, the modeling mechanism and time response function of the proposed model are expounded. A case study demonstrates that the novel model provides preferable prediction performance compared with traditional MGM(1, m) model. This work is an extension of the multivariable grey prediction model and enriches the study of grey prediction theory.

scholarly journals Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jianwei Mi ◽  
Libin Fan ◽  
Xuechao Duan ◽  
Yuanying Qiu
Keyword(s):  
Prediction Model ◽  
Load Forecasting ◽  
Exponential Smoothing ◽  
Grey Model ◽  
Grey Prediction ◽  
Short Term ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting ◽  
Exponential Smoothing Method

In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

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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