scholarly journals A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction

Energies ◽  
2016 ◽  
Vol 9 (8) ◽  
pp. 585 ◽  
Author(s):  
Qunli Wu ◽  
Chenyang Peng
Keyword(s):  
Power Generation ◽  
Support Vector Machine ◽  
Evolutionary Algorithm ◽  
Least Squares ◽  
Wind Power ◽  
Wind Power Generation ◽  
Support Vector

scholarly journals Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

2021 ◽  
Vol 13 (4) ◽  
pp. 1796
Author(s):  
Guangqi Liang ◽  
Dongxiao Niu ◽  
Yi Liang
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Evolutionary Algorithm ◽  
Least Squares ◽  
Fuzzy Ahp ◽  
Indicator System ◽  
Support Vector ◽  
Type Ii ◽  
Sustainability Evaluation ◽  
Interval Type

With the development of renewable energy, renewable energy incubators have emerged continuously. However, these incubators present a crude development model of low-level replication and large-scale expansion, which has triggered a series of urgent problems including unbalanced regional development, low incubation efficiency, low resource utilization, and vicious competition for resources. There are huge challenges for the sustainable development of incubators in the future. A scientific and accurate evaluation approach is of great significance for improving the sustainability of renewable energy incubators. Therefore, this paper proposes a novel method combining an interval type-II fuzzy analytic hierarchy process (AHP) with mind evolutionary algorithm-modified least-squares support vector machine (MEA-MLSSVM). The indicator system is established from two aspects: service capability and operational efficiency. TOPSIS integrated with an interval type-II fuzzy AHP is employed for index weighting and assessment. In the least-squares support vector machine (LSSVM), the traditional radial basis function is replaced with the wavelet transform function (WT), and the parameters are fine-tuned by the mind evolutionary algorithm (MEA). Accordingly, the establishment of a comprehensive sustainability evaluation model for renewable energy incubators is accomplished in this paper. The experimental study reveals that this novel technique has the advantages of scientificity and precision and provides a decision-making basis for renewable energy incubators to realize sustainable operation.

Load Forecasting by Considering Wind Power Based on Sequential Time Classification LSSVM Model

2013 ◽  
Vol 712-715 ◽  
pp. 2437-2440 ◽  
Author(s):  
Chen Jun Yang ◽  
Ai Hui Zhang ◽  
Hai Wei Lu ◽  
Gang Wu ◽  
Hai Yan Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Wind Power ◽  
Large Scale ◽  
Load Forecasting ◽  
Support Vector ◽  
Grid Connection ◽  
Large Scale Grid ◽  
Equivalent Load ◽  
Scale Grid

In recent years, with the large-scale grid connection of wind power, wind power as an important factor to load forecasting should not be overlooked; A least squares-support vector machine (LSSVM) has been improved for the region including wind power, based on the influence from the load caused by the changes of wind and the characteristics between load and wind power. The method uses the models of least squares-support vector machine to classify and build different models , and gets the integration of each model for equivalent load forecasting, which provides the reference for the region including wind power.

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