Accurate prediction of photovoltaic power output based on long short‐term memory network. Issue 6 (1st December 2020)

• Record Type: Journal Article
• Authors: Zhou, Nan‐Run; Zhou, Yi; Gong, Li‐Hua; Jiang, Min‐Lin
• Is Part Of: IET optoelectronics. Volume 14:Issue 6(2020)
• Page Start: 399
• Page End: 405
• Publication Date: 2020-12-01
• Subjects: load forecasting -- neural nets -- mean square error methods -- power engineering computing -- photovoltaic power systems; short‐term memory method -- prediction schemes -- photovoltaic power output prediction -- short‐term memory network -- accurate power output prediction -- photovoltaic system -- photovoltaic power sources -- prediction method -- short‐term memory neural networks; Optoelectronics -- Periodicals; 621.36
• DOI: 10.1049/iet-opt.2020.0021
• Languages: English
• ISSNs: 1751-8768

Abstract

Abstract : 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.