Design of Photovoltaic Power Generation Forecasting Model Based on Multivariable Grey Theory

2014 ◽  
Vol 953-954 ◽  
pp. 3-7 ◽  
Author(s):  
Zhi Feng Zhong ◽  
Chen Yang Yan ◽  
Tian Jin Zhang ◽  
Mao Tian
Keyword(s):  
Power Generation ◽  
Engineering Application ◽  
Theory Model ◽  
Forecasting Model ◽  
Good Prediction ◽  
Grey Theory ◽  
Power Station ◽  
Short Term ◽  
Photovoltaic Power ◽  
Short Term Prediction

This paper makes a prediction about the daily power generation of the photovoltaic power station in Wuhan International Exhibition Center by the application of multivariable grey theory model and compares the forecasted results with the real results. This research proves that the application of the theory of multivariable grey model on short-term prediction of photovoltaic power generation can achieve a good prediction effect, which also has certain engineering application value.

scholarly journals Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhifeng Zhong ◽  
Chenxi Yang ◽  
Wenyang Cao ◽  
Chenyang Yan
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Prediction Accuracy ◽  
Particle Swarm Optimization Algorithm ◽  
Theory Model ◽  
Grey Model ◽  
Grey Theory ◽  
Short Term ◽  
Swarm Optimization ◽  
Photovoltaic Power

Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV) system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.

Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method

Energy ◽  
2020 ◽  
Vol 212 ◽  
pp. 118700
Author(s):  
Chengdong Li ◽  
Changgeng Zhou ◽  
Wei Peng ◽  
Yisheng Lv ◽  
Xin Luo
Keyword(s):  
Power Generation ◽  
Accurate Prediction ◽  
Short Term ◽  
Fuzzy Method ◽  
Photovoltaic Power
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