| Issue |
E3S Web Conf.
Volume 185, 2020
2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|
|
|---|---|---|
| Article Number | 01012 | |
| Number of page(s) | 5 | |
| Section | Energy Engineering and Power System | |
| DOI | https://doi.org/10.1051/e3sconf/202018501012 | |
| Published online | 01 September 2020 | |
Improved Artificial Bee Colony Algorithm Applied to Neural Network Photovoltaic Power Generation Prediction Method
1 School of Physics And Electronic Engineering of Leshan Normal university, Leshan 614004, China
2 School of Physics And Electronic Engineering of Leshan Normal university, Leshan 614004, China
3 School of Physics And Electronic Engineering of Leshan Normal university, Leshan 614004, China
* Corresponding author’s e-mail: 29265599@qq.com
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.
© The Authors, published by EDP Sciences, 2020
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