scholarly journals Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1449 ◽  
Author(s):  
Xing Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Fuzzy Clustering ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Electric Bus ◽  
Short Term Load Forecasting ◽  
Charging Stations

scholarly journals Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Fruit Fly Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Sun ◽  
Minquan Ye
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Least Squares ◽  
Load Forecasting ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
The Stability

Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM) which is optimized by fruit fly algorithm (FOA) for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.

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.

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