Middle – Long Electric Power Load Forecasting Based on GM(1,1) and Support Vector Machine

2010 ◽  
Vol 44-47 ◽  
pp. 2983-2987
Author(s):  
Xi Miao Jia ◽  
Guo Ping Song ◽  
Ting Wang ◽  
Feng Kong
Keyword(s):  
Support Vector Machine ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Difficult Problem ◽  
Support Vector ◽  
Load Forecast ◽  
Power Load ◽  
Long Time ◽  
Load Vector

Due to the variety and the randomicity of its influencing factors, the electricity demand forecasting is a difficult problem for a long time. In order to improve the forecast accuracy, the paper proposes a new load forecast model based on GM(1,1) and support vector machine. First, the GM(1,1) is used to forecast the load data in the model. And then according to factors and historical load vector, support vector machine load forecast model is established to forecast the residuals of GM(1,1) and modified the forecast results of GM(1,1). Case analysis shows that the forecast method is suitable and effective, improving prediction precision compared with GM(1,1) and support vector machine, and has better utility value in mid-log term load forecast.

Prediction Model of Least Squares Support Vector Machine of Increased Memory Type Based on GA and Quadratic Renyi-Entropy

2011 ◽  
Vol 219-220 ◽  
pp. 754-761
Author(s):  
Guan Hua Zhao ◽  
Wen Wen Yan
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Financial Distress ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Support Vector ◽  
Memory Type

In order to improve the accuracy of financial achievement, this paper applies a new forecast model of the Increased memory type least squares support vector machine base on neighborhood rough set and quadratic Renyi-entropy on the basis of the traditional support vector machine prediction model. The paper also independently derives the entropy fit for the financial distress prediction which is in discrete sequence, as well as the expression of support vector machine kernel function. The experimental results show that the improved model is significantly superior to the traditional LS-SVM as well as the standard support vector machine prediction model, regardless of the forecast accuracy , training samples number.

Monthly Load Forecasting Based on Optimum Grey Model

2011 ◽  
Vol 230-232 ◽  
pp. 1226-1230
Author(s):  
Ting Wang ◽  
Xi Miao Jia
Keyword(s):  
Historical Data ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Difficult Problem ◽  
Short Term ◽  
Original Series ◽  
Short Term Load Forecasting ◽  
Calculation Results ◽  
Long Time

Due to the variety and the randomicity of its influencing factors, the monthly load forecasting is a difficult problem for a long time. In order to improve the forecast accuracy, the paper proposes a new load forecast model based on improved GM (1, 1).First, the GM (1, 1) is used to forecast the load data, which takes the longitude historical data as original series, the increment trend of load was forecasted and takes the crosswise historical data as original series, the fluctuation trend of load was forecasted. On this basis the optimum method is led in. An optimal integrated forecasting model is built up. The case calculation results show that the proposed method can remarkably improve the accuracy of monthly load forecasting, and decrease the error. The integrated model this paper describes for short-term load forecasting is available and accurate.

scholarly journals Short-term power load forecasting based on support vector machine and particle swarm optimization

2018 ◽  
Vol 13 ◽  
pp. 174830181879706 ◽  
Author(s):  
Song Qiang ◽  
Yang Pu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Influencing Factors ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Model Parameters ◽  
Short Term ◽  
Swarm Optimization

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the “abnormal data” in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.

scholarly journals A support vector machine for model selection in demand forecasting applications

2018 ◽  
Vol 121 ◽  
pp. 1-7 ◽  
Author(s):  
Marco A. Villegas ◽  
Diego J. Pedregal ◽  
Juan R. Trapero
Keyword(s):  
Support Vector Machine ◽  
Model Selection ◽  
Demand Forecasting ◽  
Support Vector
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