Medium and Long-Term Forecasting Method of China’s Power Load Based on SaDE-SVM Algorithm

2018 ◽  
pp. 484-495
Author(s):  
Yuansheng Huang ◽  
Lijun Zhang ◽  
Mengshu Shi ◽  
Shijian Liu ◽  
Siyuan Xu
Keyword(s):  
Power Load ◽  
Forecasting Method ◽  
Svm Algorithm ◽  
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 Application of Combination Forecast Model in the Medium and Long Term Power Load Forecast

2012 ◽  
Vol 490-495 ◽  
pp. 1362-1366 ◽  
Author(s):  
Ke Zhao ◽  
Lin Gan ◽  
Zhong Wang ◽  
Yan Xiong
Keyword(s):  
Load Forecasting ◽  
Forecast Model ◽  
Combination Forecast ◽  
Combination Forecasting ◽  
Power Load ◽  
Forecasting Method ◽  
Model Predictions ◽  
High Prediction ◽  
Predicted Values

For seasonal and long-term power load forecasting problem, this paper presents an optimal combination forecasting method, which can optimize the combination of multiple predictive models. Optimize the combination of the two model predictions with two models as an example, which are the gray GM(1,1) model and linear regression model, and finally compare the predicted values of combination with the real values. The results show that: the combination forecasting method has a high prediction accuracy, and the error is very small.

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.

The Mid-Long Term Power Load Forecasting Based on Gray and SVM Algorithm

2010 ◽  
Vol 143-144 ◽  
pp. 1164-1169 ◽  
Author(s):  
Wei Li ◽  
Zhen Gang Zhang ◽  
Ning Yan
Keyword(s):  
Load Forecasting ◽  
Wave Characteristics ◽  
Power Load ◽  
Svm Algorithm ◽  
Power Load Forecasting ◽  
Growth Properties ◽  
Nonlinear Relation ◽  
Single Method ◽  
Better Than

Generally, the power load forecasting sequence has stochastic growth and nonlinear wave characteristics, grey SVM can effective reflect the growth properties of the sequence and fit the nonlinear relation. The whole forecasting precision of the sequence was optimized, and the transfer matrix for the forecasting sequence was decided, then the accuracy for power load forecasting was greatly improved. Through the demonstration test, the precision is better than single method, the method in this paper have feasibility in practice.

Sign in / Sign up

Export Citation Format

Share Document