Peng Yan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Peng Yan has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Chenmeng Xiang
State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China
1 shared publication - Lei Wang
State Grid Hebei Electric Power Research Institute, Shijiazhuang, China
1 shared publication - Liang Meng
State Grid Hebei Electric Power Research Institute, Shijiazhuang, China
1 shared publication - Tiecheng Li
Shanghai Institute of Measurement and Testing Technology (China)
1 shared publication - Wen Zhou
State Grid Hebei Electric Power Research Institute, Shijiazhuang, China
1 shared publication - Xuekai Hu
State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China
1 shared publication
Latest Publications
- RetractedOpen Access
- 1 February 2022
- Published by Wiley in Computational Intelligence and Neuroscience
- Vol. 2022, 1-11
Research on Probability Distribution of Short-Term Photovoltaic Output Forecast Error Based on Numerical Characteristic Clustering
The forecast error characteristic analysis of short-term photovoltaic power generation can provide a reliable reference for power system optimal dispatching. In this paper, the total in-day error level was stratified by fuzzy C-means algorithm. Then the historical PV output data based on the numerical characteristics of point prediction output were classified. A General Gauss Mixed Model was proposed to fit the forecast error distribution of various photovoltaic output forecast error distribution. The impact of meteorological factors together with numerical characteristics on the forecast error was taken into full consideration in this analysis method. The predicted point output with high volatility can be accurately captured, and the reliable confidence interval is given. The proposed method is independent of the point prediction algorithm and has strong applicability. The General Gauss Mixed Model can meet the peak diversity, bias, and multimodal properties of the error distribution, and the fitting effect is superior to the normal distribution, the Laplace distribution, and the t Location-Scale distribution model. The error model has a flexible shape, a concise expression, and high practical value for engineering.
- 1 February 2022
- Published by Wiley in Computational Intelligence and Neuroscience
- Vol. 2022, 1-11
RetractedOpen AccessResearch on Probability Distribution of Short-Term Photovoltaic Output Forecast Error Based on Numerical Characteristic Clustering
The forecast error characteristic analysis of short-term photovoltaic power generation can provide a reliable reference for power system optimal dispatching. In this paper, the total in-day error level was stratified by fuzzy C-means algorithm. Then the historical PV output data based on the numerical characteristics of point prediction output were classified. A General Gauss Mixed Model was proposed to fit the forecast error distribution of various photovoltaic output forecast error distribution. The impact of meteorological factors together with numerical characteristics on the forecast error was taken into full consideration in this analysis method. The predicted point output with high volatility can be accurately captured, and the reliable confidence interval is given. The proposed method is independent of the point prediction algorithm and has strong applicability. The General Gauss Mixed Model can meet the peak diversity, bias, and multimodal properties of the error distribution, and the fitting effect is superior to the normal distribution, the Laplace distribution, and the t Location-Scale distribution model. The error model has a flexible shape, a concise expression, and high practical value for engineering.