Investigating the Predictability of Photovoltaic Power Using Approximate Entropy

• Main Authors: Mao Yang, Kaixuan Wang, Yang Cui, Fan Feng, Xin Su, Chenglian Ma
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-05-01
• Series: Frontiers in Energy Research
• Subjects: PV power predictability; approximate entropy; clustering effect; time series; weather type
• Online Access: https://www.frontiersin.org/articles/10.3389/fenrg.2021.681494/full

The predictability concept of Photovoltaic (PV) power on the time series was presented and the approximate entropy algorithm and predictable coefficient were used to quantificationally analyze the predictability of PV power on time series, then the approximate entropy and predictable coefficient variation at different spatial scale were analyzed. Finally, the measured data of a PV plant in western Ningxia were used for testing and confirming the result. The results of several typical prediction methods show that the proposed method can effectively characterize the predictability of PV power on time series.