scholarly journals Electrical Load Prediction for Short Term using Support Vector Machine Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 111-116
Keyword(s):  
Support Vector Machine ◽  
Prediction Models ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Electrical Load ◽  
Load Prediction ◽  
Short Term ◽  
Training Time ◽  
Short Term Load Forecasting

The electrical load prediction during an interval of a week or a day plays an important role for scheduling and controlling operations of any power system. The techniques which are presently being used and are used for Short Term Load Forecasting (STLF) by utilizing various prediction models try for the performance improvement. The prediction models and their performance mainly depend upon the training data and its quality. The different forecasting approaches using Support Vector Machine (SVM) depending on several performance indices has been discussed. The accuracy of the forecasting approaches is measured by Mean Absolute Error (MAE), Root Mean Square Error (RMSE), prediction speed and training time. The approach with least RMSE reveals as the best among the SVM methods for short term load forecasting.

Power Short-Term Load Forecasting Based on QPSO-SVM

2012 ◽  
Vol 591-593 ◽  
pp. 1311-1314
Author(s):  
Xing Tong Zhu ◽  
Bo Xu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Quantum Particle ◽  
Close Contact ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Short Term Load Forecasting

The values of parameters of support vector machine have close contact with its forecast accuracy. In order to accurately forecast power short-term load,we presented a power short-term load forecasting method based on quantum-behaved particle swarm optimization and support vector machine.First,cauchy distribution was used to improve the quantum particle swarm algorithm.Secondly,the improved quantum particle swarm optimization algorithm was used to optimize the parameter of support vector machine.Finally, the support vector machine was used for power short-term load forecasting. In the proposed method such factors impacting loads as meteorology,weather and date types are comprehensively considered. The experimental results show that the root-mean-square relative error of the proposed method is only 1.90%, which is less than those of SVM and PSO-SVM model by 2.29% and 2.80%, respectively.

A Short-Term Load Forecasting Method of Warship Based on PSO-SVM Method

2011 ◽  
Vol 127 ◽  
pp. 569-574
Author(s):  
Dong Liang Li ◽  
Xiao Feng Zhang ◽  
Ming Zhong Qiao ◽  
Gang Cheng
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Power Load ◽  
Forecasting Method ◽  
Short Term Load Forecasting ◽  
Adaptive Pso

The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.

Sign in / Sign up

Export Citation Format

Share Document