Correction of Predictive Power for Photovoltaic Plant based on Meteorological and Geographical Correlations

2018 International Conference on Advanced Technologies in Energy, Environmental and Electrical Engineering

能源学;生态环境科学;无线电电子学

Guo, Hui^1 ; Yang, Guoqing^1 ; Yao, Lixiao^1 ; Zhang, Shujie^2

Institute of Water Resource and Hydro-electric Engineering, Xi'An University of Technology, Xi'an, Shaanxi

710048, China^1

State Grid Qinghai Electric Power Company Electric Power Research Institute, Xining, Qinghai

810000, China^2

关键词: BP neural network model;    BP neural networks;    Curve similarities;    Geographical correlation;    Geographical positions;    Grey relational analysis;    PhotoVoltaic plant;    Product-moment correlation;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/223/1/012006/pdf
DOI  :  10.1088/1755-1315/223/1/012006

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

A abnormal data repairing method based on adjacent power plant and integrated similar days of BP neural network is presented. Some factors influencing power generation such as geographical position, temperature and day type are considered. By means of selecting adjacent power plants with high power correlation to plant to be repaired by Pearson product-moment correlation coefficient and the combination of Grey Relational Analysis and curve similarity is used to select similar days. Finding out the integrated similar days' data of the adjacent power plant that is in conformity with the day to be repaired, corresponding BP neural network model is built, then the diverse learning speed algorithm are employed to repair abnormal data. The actual abnormal data in PV prediction power repairing results for Qinghai district show that the proposed method possesses better repairing accuracy.