Open AccessShort-term Global Horizontal Irradiance Prediction Based on Deep Echo State Network
Author(s) -
Gaohong Ma,
Jun Li
Publication year - 2022
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 - 10.1088/1742-6596/2171/1/012028
Subject(s) - irradiance , term (time) , photovoltaic system , solar irradiance , echo state network , echo (communications protocol) , computer science , environmental science , meteorology , artificial intelligence , artificial neural network , engineering , optics , geography , recurrent neural network , physics , electrical engineering , computer network , quantum mechanics
The prediction of global horizontal irradiance have a great impact on the stability and economic benefits of photovoltaic (PV) power generation. In this paper, we adopt the method of Deep Echo State Network (DESN) to predict the global horizontal irradiance in different areas one hour in advance. Under the same conditions, the results show that DESN are better than BP, SVM, ESN methods in the prediction accuracy. Experiments show that the proposed models show superior ability in predicting solar irradiance and have great application potential in power grid integration.