Study on Mid-Long Term Forecasting Method of Power Supply Based on Grey Model and Markov Forecasting Model

2018 ◽  
Author(s):  
Huang Qinqin ◽  
Li Xiangming ◽  
Leng Zhiwen ◽  
Qi Yue ◽  
Liu Hanjie
Keyword(s):  
Power Supply ◽  
Grey Model ◽  
Forecasting Model ◽  
Forecasting Method ◽  
Long Term Forecasting

scholarly journals Comparison of the long-term forecasting method of RSSI by machine learning

2020 ◽  
Vol 9 (11) ◽  
pp. 553-558
Author(s):  
Tatsuya Nagao ◽  
Takahiro Hayashi ◽  
Yoshiaki Amano
Keyword(s):  
Machine Learning ◽  
Forecasting Method ◽  
Long Term Forecasting

scholarly journals Geometrical Variable Weights Buffer GM(1,1) Model and Its Application in Forecasting of China’s Energy Consumption

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Wei Li ◽  
Han Xie
Keyword(s):  
Energy Consumption ◽  
Maximum Degree ◽  
Original Data ◽  
Grey Model ◽  
Variable Weights ◽  
Proposed Model ◽  
Term Energy ◽  
Long Term Forecasting ◽  
The Stability

In order to improve the application area and the prediction accuracy of GM(1,1) model, a novel Grey model is proposed in this paper. To remedy the defects about the applications of traditional Grey model and buffer operators in medium- and long-term forecasting, a Variable Weights Buffer Grey model is proposed. The proposed model integrates the variable weights buffer operator with the background value optimized GM(1,1) model to implement dynamic preprocessing of original data. Taking the maximum degree of Grey incidence between fitting value and actual value as objective function, then the optimal buffer factor is chosen, which can improve forecasting precision, make forecasting results embodying the internal trend of original data to the maximum extent, and improve the stability of the prediction. To verify the effectiveness of the proposed model, the energy consumption in China from 2002 to 2009 is used for the modeling to forecast the energy consumption in China from 2010 to 2020, and the forecasting results prove that the GVGM(1,1) model has remarkably improved the forecasting ability of medium- and long-term energy consumption in China.

scholarly journals LONG-TERM FORECASTING OF EXTRAORDINARY SPRING FLOODS BY СOMMENSURABILITY METHOD ON THE DNIPRO RIVER NEAR THE KYIV CITY, UKRAINE

2019 ◽  
Vol 75 (2) ◽  
pp. 74-81
Author(s):  
Borys Fedorovich Khrystiuk ◽  
Liudmyla Olexandrivna Gorbachova
Keyword(s):  
Lead Time ◽  
Quantitative Methods ◽  
Practical Significance ◽  
The Past ◽  
Forecasting Method ◽  
Forecast Lead Time ◽  
Information Method ◽  
Long Term Forecasting ◽  
Industrial Center

The Kyiv city is the capital of Ukraine, as well as its major administrative and industrial center. Kyiv is located in the middle reaches of the Dnipro River which is the largest river in Ukraine. In the past, the Kyiv city suffered from dangerous spring floods. Consequently, long-term forecasting of spring floods on the Dnipro River near Kyiv has an important scientific and practical significance. Existing quantitative methods for such forecasting are of limited forecast lead time and require many input hydrometeorological data. In the paper the information method Weng Wen-Bo applied, which is a qualitative forecasting method. The use such method allows to determine the periods and specific years in which the following extraordinary spring floods on the Dnipro River near Kyiv can occur.

scholarly journals Medium—And Long-Term Load Forecasting Method for Group Objects Based on the Image Representation Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Daolu Zhang ◽  
Weiling Guan ◽  
Jiajun Yang ◽  
Huang Yu ◽  
WenCong Xiao ◽  
...  
Keyword(s):  
Distribution Network ◽  
Image Representation ◽  
Load Forecasting ◽  
Representation Learning ◽  
Forecasting Model ◽  
Distribution Transformer ◽  
Change Rate ◽  
Forecasting Method ◽  
Trend Forecasting

Medium-and long-term load forecasting in the distribution network has important guiding significance for overload warning of distribution transformer, transformation of distribution network and other scenarios. However, there are many constraints in the forecasting process. For example, there are many predict objects, the data sample size of a single predict object is small, and the long term load trend is not obvious. The forecasting method based on neural network is difficult to model due to lack of data, and the forecasting method based on time sequence law commonly used in engineering is highly subjective, which is not effective. Aiming at the above problems, this paper takes distribution transformer as the research object and proposes a medium-and long-term load forecasting method for group objects based on Image Representation Learning (IRL). Firstly, the data of distribution transformer is preprocessed in order to restore the load variation in natural state. And then, the load forecasting process is decoupled into two parts: the load trend forecasting of the next year and numerical forecasting of the load change rate. Secondly, the load images covering annual and inter-annual data change information are constructed. Meanwhile, an Image Representation Learning forecasting model based on convolutional neural network, which will use to predict the load development trend, is obtained by using load images for training; And according to the data shape, the group classification of the data in different periods are carried out to train the corresponding group objects forecasting model of each group. Based on the forecasting data and the load trend forecasting result, the group forecasting model corresponding to the forecasting data can be selected to realize the numerical forecasting of load change rate. Due to the large number of predict objects, this paper introduces the evaluation index of group forecasting to measure the forecasting effect of different methods. Finally, the experimental results show that, compared with the existing distribution transformer forecasting methods, the method proposed in this paper has a better overall forecasting effect, and provides a new idea and solution for the medium-and long-term intelligent load forecasting of the distribution network.

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