Short-term prediction model of module temperature for photovoltaic power forcasting based on support vector machine

2015 ◽  
Author(s):  
Yujing Sun ◽  
Fei Wang ◽  
Zenggiang Mi ◽  
Zhao Zhen ◽  
Hongbin Sun ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Support Vector ◽  
Short Term ◽  
Photovoltaic Power ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Very short-term prediction model for photovoltaic power based on improving the total sky cloud image recognition

2017 ◽  
Vol 2017 (13) ◽  
pp. 1947-1952 ◽  
Author(s):  
Zhu Xiang ◽  
Wu Ji ◽  
Zhou Hai ◽  
Ding Jie ◽  
Cui Fang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Image Recognition ◽  
Short Term ◽  
Photovoltaic Power ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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