Papers by Keyword: Grey Modeling

Paper TitlePage

Authors: Hong Jing Zhang, Feng Wang, Zhen Kun Tian
Abstract: China's electricity imports and its influence factors of ten years were analyzed. On this basis, the electricity imports and the influence factors of grey correlation were calculated by using grey correlation analysis method. It makes a conclusion that every influence factors effect on electricity imports, and establishes a grey model GM (1, n) of electricity imports as well as its influence factors and determines the quantitative relationship between the selected influence factors and electricity imported. The model has high accuracy, and meets the practical requirements, so that it makes a better load forecasting for our country and provides a theoretical basis for taking some electricity import measures.
2158
Authors: De Gang Liao, You Xin Luo
Abstract: The accuracy of the traditional grey model is not high for the monotonic decreasing data. The paper uses the opposite-direction accumulated method and makes full use of new information combined with optimization of the background values. It induces the formula of the parameters in the model and establishes the grey new information GOM(1,1) model in which it is based on the opposite-direction accumulated operation and optimization of the background values. It is a new method of the grey model. The examples show it is with higher accuracy than that of non-new information model.
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Authors: Xiao Yi Che, You Xin Luo, Zhe Ming He
Abstract: As the monotonic decreasing data the traditional grey model is characteristic with low accuracy, using the opposite-direction accumulated generating operation, making full use of the new information, the formula of the parameters was induced and the grey new information GOM(1,1) model was established based on opposite-direction accumulated generating operation. It provides a new way for the grey model. The examples show the model is practical and reliable.
207
Authors: Yue Hua Cai, Wei Yue Xiao, Bin Zeng
Abstract: As the monotonic decreasing data the traditional grey model is characteristic with low accuracy, using reciprocal generating operation, making full use of the new information, optimizing the background values, the formula of the parameters was induced and the grey new information GRM(1,1) model was established based on reciprocal accumulating and background value optimization. It provides a new way for the grey model. The examples show the new information model has high accuracy than the non-new information model.
188
Authors: You Xin Luo
Abstract: Monotonically decreasing sequence data for the traditional modeling method using the gray model accuracy is not high, using Accumulated Generating Operation in reciprocal number, utilize three gray derivative processing method is deduced and the parameters optimization grey derivative calculation formula, and then established GRM(1,1) based on accumulated generating operation in reciprocal number on the equidistance, Gray provides a new method of modeling. Data processing examples show that the model's practicality and reliability.
77
Authors: W.Y. Xiao, Y.Y. Luo, Xiao Yi Che
Abstract: Monotonically decreasing sequence data for the traditional modeling method using the gray model accuracy is not high , and GM (1, 1) modeling method has inherent deviation , Model does not meet the compatibility condition, using Accumulated Generating Operation in reciprocal number ,make best use of last information and GM (1, 1) modeling , is deduced and the parameters optimization grey derivative calculation formula ,and then established GRM(1,1) based on accumulated generating operation in reciprocal number on the equidistance, Gray provides a new method of modeling . Data processing examples show that the model's practicality and reliability.
81
Authors: De Gang Liao, You Xin Luo
Abstract: The accuracy of the grey model is not high for the monotonic decreasing data and the error can not avoided. The model can not satisfy the compatibility conditions and then it is not accordant with the linear transformation. The paper adopts the equal interval unbiased model and takes one of the components in as the initial values. By this method it induces the parameters in the grey unbiased GRM(1,1) model and establishes the equal interval unbiased model based on the reciprocal AGO process. The model has high accuracy and has good value in the theory and practice. The examples show it is practical and reliable.
29
Authors: Yong Li, Zhan Wu Wang, Shuang Ning Tang
Abstract: The paper is committed to overcome the influence of gross error on the small quantity data of forest fire grey modeling. According to the quantity of the modeling data, Grey judgment of gross error and robust estimation theory is used separately for finding the gross error exit whether or not from the modeling data. And robust estimation theory and LIR algorithm can be used to process the gross error. From the examples, A quarter of fitting precision of robust estimation is less than 1%, and 75% is 1~5%; and half of fitting precision of LIR algorithm is less than 1%, and half is 1~5%. That is to say LIR algorithm provides a rapid, simple and practical way to build model of data which contains gross error or which contain missing data.
1262
Authors: Xiang Shuo He, Li Yang, Xiao Na Yu
Abstract: It is well known that mid and long term electric load forecasting has many uncertain factors that influence the forecasting precision greatly, so every forecasting method has its limitation. Considering limitations of basic grey model and conventional improved models, a new practical method called combined optimum grey model for mid and long term load forecasting is introduced. The combined model is composed of partial error optimum grey model (GM) as well as equa-l dimension and new-information grey model. The forecasting algorithm can estimate model parameters, meet the requirements of dynamic power load and overcome random disturbances. Example analysis shows that the forecasting error is below 3 percent. Compared with conventional theoretical methods, the proposed scheme has the characters of simple computation, high forecasting precision and good applicability.
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