Open AccessAccurate prediction of photovoltaic power output based on long short‐term memory network
Author(s) -
Zhou NanRun,
Zhou Yi,
Gong LiHua,
Jiang MinLin
Publication year - 2020
Publication title -
iet optoelectronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.379
H-Index - 42
eISSN - 1751-8776
pISSN - 1751-8768
DOI - 10.1049/iet-opt.2020.0021
Subject(s) - photovoltaic system , artificial neural network , computer science , term (time) , mean squared error , power (physics) , sine , control theory (sociology) , algorithm , mathematics , engineering , statistics , artificial intelligence , control (management) , electrical engineering , physics , quantum mechanics , geometry
An accurate power output prediction of the photovoltaic system is pivotal to eliminate the extra cost and the negative impact in the utility grid integrated with photovoltaic power sources. The power output of a photovoltaic system is predicted by introducing a long short‐term memory method. Moreover, the influence of noise data on prediction results is eliminated with the empirical mode decomposition. To further improve the accuracy and stability of the prediction method, the parameters of long short‐term memory neural networks are determined with a sine cosine algorithm. The performances of the long short‐term memory method in terms of root mean square error, mean absolute error, and coefficient of determination in January and August are analysed, respectively. Compared with other prediction schemes, the long short‐term memory method provides superior accuracy for photovoltaic power output prediction.