Open AccessUltra-short-term PV Power Forecasting Based on Cloud Image Modeling
Author(s) -
Tianhao Zhang,
Jinxiang Deng,
Zhongxue Gan,
Hong Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1746/1/012007
Subject(s) - photovoltaic system , brightness , computer science , term (time) , power (physics) , cloud computing , sky , environmental science , real time computing , meteorology , engineering , electrical engineering , optics , physics , quantum mechanics , operating system
Due to the impact of many local sudden changes in photovoltaic (PV) output power, its ultra-short-term forecasting is facing great challenges. In a time scale of 1 to 2 minutes, the generation, movement, and ablation of clouds in the sky are the main factors that affect the output power. Therefore, a prediction method based on cloud images is proposed for ultra-short-term prediction of PV output power. First, apply image processing technology to extract image features that affect the changes in PV output power, including cloud coverage rate, direct sunlight rate, and image brightness; second, calculate the extraterrestrial irradiation and air mass at each time point; finally, use the above features as input factors, and use PV output power output data, and a long short-term memory network (LSTM) is used to construct a PV power prediction model to achieve ultra-short-term forecasting of PV output power.