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Media type:
E-Article
Title:
Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data
Contributor:
Jeon, Byung-ki;
Kim, Eui-Jong
Published:
MDPI AG, 2020
Published in:
Energies, 13 (2020) 20, Seite 5258
Language:
English
DOI:
10.3390/en13205258
ISSN:
1996-1073
Origination:
Footnote:
Description:
Solar irradiance prediction is significant for maximizing energy-saving effects in the predictive control of buildings. Several models for solar irradiance prediction have been developed; however, they require the collection of weather data over a long period in the predicted target region or evaluation of various weather data in real time. In this study, a long short-term memory algorithm–based model is proposed using limited input data and data from other regions. The proposed model can predict solar irradiance using next-day weather forecasts by the Korea Meteorological Administration and daily solar irradiance, and it is possible to build a model with one-time learning using national and international data. The model developed in this study showed excellent predictive performance with a coefficient of variation of the root mean square error of 12% per year even if the learning and forecast regions were different, assuming that the weather forecast was correct.