| Issue |
E3S Web Conf.
Volume 185, 2020
2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|
|
|---|---|---|
| Article Number | 02028 | |
| Number of page(s) | 9 | |
| Section | Energy Saving and Environmental Protection Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202018502028 | |
| Published online | 01 September 2020 | |
Study on Ultra-short-time Power Forecast of Photovoltaic System based on Ground-based Cloud Image Recognition and Key Impact Factors
1 College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China
2 Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou, Gansu, 730000, China
* Corresponding author: psaswangyiying@163.com
In recent years, under the dual pressure of resource shortage and environmental pollution, the photovoltaic (PV) power generation industry has flourished. The irradiance forecasting technology of PV power plants is of great significance for output prediction, grid dispatching and safe operation. Cloud cover is always the key factor making the irradiance fluctuate. In this article, colorful ground-based cloud images are collected by the all-sky imager every minute as the research object. Based on the traditional threshold method, a hybrid entropy threshold method is proposed to identify cloud clusters. Using the correlation analysis, among many impact factors with high correlation, five are extracted as input parameters of a BP network optimized by genetic algorithm (GA-BP). Through verification and comparison analysis, it is concluded that the recognition accuracy of the hybrid entropy threshold method is higher, and the average relative error can be controlled at about 5%. Based on this, the irradiance prediction of GA-BP also achieved better results than other models. It can meet the application requirements of PV power plants.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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