Photovoltaic Power Generation Prediction Based on Correlation Analysis and GA - BP Neural Network

2021 ◽  
Author(s):  
Ji Zeng ◽  
Cong Wang ◽  
XiuQiong Hu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Correlation Analysis ◽  
Bp Neural Network ◽  
Photovoltaic Power

scholarly journals Improved Artificial Bee Colony Algorithm Applied to Neural Network Photovoltaic Power Generation Prediction Method

2020 ◽  
Vol 185 ◽  
pp. 01012
Author(s):  
Jiaxiong Zhu ◽  
Jiang Qiang ◽  
Chang Feng ◽  
Cao Jing
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Prediction Method ◽  
Weather Conditions ◽  
Network Parameter ◽  
Bee Colony ◽  
Photovoltaic Power

With the increase in the use of renewable energy, especially the development and utilization of solar energy resources, accurate photovoltaic power generation prediction technology will help the promotion of photovoltaic power generation. The amount of photovoltaic power generation depends on weather conditions, and it is easy to produce large fluctuations under different weather conditions. Its power generation has the characteristics of randomness, fluctuation and intermittency. In view of the shortcomings of the traditional BP neural network prediction method, this paper proposes an improved artificial bee colony algorithm. The improved artificial bee colony algorithm is used to optimize the network parameter weights in the traditional BP algorithm, and the two algorithms are merged in global iteration. Based on the characteristics of training light intensity, weather, temperature and historical power value of photovoltaic output power,a photovoltaic power generation prediction model is established. The simulation results show that the improved artificial bee colony algorithm in the neural network’s photovoltaic power generation forecast improves the accuracy and convergence speed of the traditional BP neural network convergence solution, and can provide more comprehensive information for grid power dispatch and control.

scholarly journals Applying a Cerebellar Model Articulation Controller Neural Network to a Photovoltaic Power Generation System Fault Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Bo-Jyun Liao ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Diagnosis ◽  
Cerebellar Model Articulation Controller ◽  
Training Process ◽  
Cmac Neural Network ◽  
Photovoltaic Power ◽  
Training Samples ◽  
Parallel Connections ◽  
Model Features

This study employed a cerebellar model articulation controller (CMAC) neural network to conduct fault diagnoses on photovoltaic power generation systems. We composed a module array using 9 series and 2 parallel connections of SHARP NT-R5E3E 175 W photovoltaic modules. In addition, we used data that were outputted under various fault conditions as the training samples for the CMAC and used this model to conduct the module array fault diagnosis after completing the training. The results of the training process and simulations indicate that the method proposed in this study requires fewer number of training times compared to other methods. In addition to significantly increasing the accuracy rate of the fault diagnosis, this model features a short training duration because the training process only tunes the weights of the exited memory addresses. Therefore, the fault diagnosis is rapid, and the detection tolerance of the diagnosis system is enhanced.

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