期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 9, 期 2, 页码 538-546出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2017.2747765
关键词
Autoregressive processes; forecasting; photo-voltaic systems; smart grids; spatial correlation; stationarity; time series
In recent years, the penetration of photovoltaic (PV) generation in the energy mix of several countries has significantly increased thanks to policies favoring development of renewables and also to the significant cost reduction of this specific technology. The PV power production process is characterized by significant variability, as it depends on meteorological conditions, which brings new challenges to power system operators. To address these challenges, it is important to be able to observe and anticipate production levels. Accurate forecasting of the power output of PV plants is recognized today as a prerequisite for large-scale PV penetration on the grid. In this paper, we propose a statistical method to address the problem of stationarity of PV production data, and develop a model to forecast PV plant power output in the very short term (0-6 h). The proposed model uses distributed power plants as sensors and exploits their spatio-temporal dependencies to improve forecasts. The computational requirements of the method are low, making it appropriate for large-scale application and easy to use when online updating of the production data is possible. The improvement of the normalized root mean square error (nRMSE) can reach 20% or more in comparison with state-of-the-art forecasting techniques.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据