The Method for Photovoltaic Module Temperature Ultra-short-term Forecasting Based on RBF Neural Network

摘要：

　　As module temperature plays an important role in the conversion efficiency of photovoltaic(PV) module, an accurate prediction will be very helpful in improving PV power forecasting. In this article, a method of ultra-short-term forecasting for PV module temperature based on RBF neural network was proposed, which had a prediction aging of the next 4 hour, time resolution of prediction point was 15-minute, and prediction rolling cycle of 15-minutes. In the case study, this approach continuously provides reasonable temperature forecasting of the PV module. It is indicated that this approach is of significance in practical application.

关键词： module temperature photovoltaic RBF neural network ultra-short-term forecasting

作者: Xu Cheng Furong Chen Bingxia Yu Xuesong Zhang

作者单位: China Electric Power Research Institute State Grid Nanjing Power Supply Company State Grid Zhejiang Electric Power Research Institute

会议类型: 国际会议

会议名称: 2016中国国际供电会议(CICED2016)

会议地点: 西安

会议语种:英文

页码: 1-5

在线出版日期: 2016-09-01（万方平台首次上网日期，不代表论文的发表时间）