Issue |
E3S Web Conf.
Volume 256, 2021
2021 International Conference on Power System and Energy Internet (PoSEI2021)
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Article Number | 02004 | |
Number of page(s) | 6 | |
Section | Energy Internet R&D and Smart Energy Application | |
DOI | https://doi.org/10.1051/e3sconf/202125602004 | |
Published online | 10 May 2021 |
Research on the error probability distribution of photovoltaic output prediction based on output fluctuation characteristics and Generalized Gaussian Mixture Model
State Grid Hebei Electric Power Research Institute, Shijiazhuang, Hebei Province, 050021, China
* Corresponding author’s e-mail: dyy_yanp@he.sgcc.com.cn
Photovoltaic power output forecast error exists objectively and inevitably, and it can provide a guarantee for safe and stable operation of the power system through analyzing its characteristics. In this paper, the influence of predicted output fluctuation characteristics (predicted output amplitude and power variation) on prediction error was studied based on the analysis of variance (ANOVA) method. The prediction error conditions were classified into six types based on the clustering of numerical characteristics of predicted output. Then, a Generalized Gaussian Mixture Model (GGMM) was proposed to fit the prediction error distribution of each type of photovoltaic output. The mean absolute error (MAE), coefficient of determination (R2), and root mean square error (RMSE) were used as accuracy evaluation indexes. The example analysis showed that the GGMM can satisfy the asymmetry and kurtosis diversity of the error distribution after division by conditions, and the fitting result is better than that of the normal distribution, improved Laplace distribution and t Location-Scale distribution model.
© The Authors, published by EDP Sciences, 2021
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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