Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks

• 作者: Lukas WURZER Saad Parvaiz DURRANI Stefan BALLUFF Stefan KRAUTER
• 来源期刊: 现代电力系统与清洁能源学报(英文)
• 学科: 工学
• 关键词: Grid CONNECTED photovoltaic(GCPV) Photovoltaic(PV) PV power prediction IRRADIANCE FORECAST Neural network(NN)
• 年，卷（期）: 2018,（2）
• 页码范围: 255-267
• 页数: 13页
• 分类号: TM615

摘要

In order to develop predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems, accurate and reliable photovoltaic(PV) power forecasts are required.A PV yield prediction system is presented based on an irradiance forecast model and a PV model. The PV power forecast is obtained from the irradiance forecast using the PV model. The proposed irradiance forecast model is based on multiple feed-forward neural networks. The global horizontal irradiance forecast has a mean absolute percentage error of 3.4% on a sunny day and 23% on a cloudy day for Stuttgart. PV power forecasts based on the neural network irradiance forecast have performed much better than the PV power persistence forecast model.