An approach for day-ahead interval forecasting of photovoltaic power: A novel DCGAN and LSTM based quantile regression modeling method
Zhenhao Wang, Chong Wang, Long Cheng, Guoqing Li
Abstract
In order to effectively quantify the uncertainty of PV power and improve the forecasting accuracy, a day-ahead interval forecasting method of PV power based on multi-correlation parameter scenarios generation is proposed. The historical PV power data is divided into a limited set of scenarios representing different output and fluctuation characteristics through the K-means clustering algorithm; Combined with the strong correlation feature parameters determined by the correlation coefficient, the characteristic curves of PV power and multi-correlation parameter in different scenarios are generated through the deep convolutional generative adversarial networks (DCGAN) model; The average value of each characteristic curve and other explanatory variables are input together into a quantile regression long short-term memory (QRLSTM) model to achieve day-ahead PV power forecasting, and obtain the forecasting interval under different confidence levels. The results of the experiment of a PV power station in Yangzhou, Jiangsu Province, China show that the forecasting performance of the proposed method under different evaluation indicators is 17.9755% and 18.1571% higher than that of the Gaussian process regression (GPR) model and the single QRLSTM model, respectively, which has obvious advantages.