Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data

• Published in: Energies (Basel) 2020-10, Vol.13 (20), p.5258
• Main Authors: Jeon, Byung-ki, Kim, Eui-Jong
• Format: Article
• Language: English
• Subjects: Accuracy; Air conditioning; Coefficient of variation; Control algorithms; Deep learning; Energy; Energy conservation; Irradiance; Learning; Long short-term memory; Meteorological data; Neural networks; Physics; Predictions; solar irradiance; Weather forecasting; weather prediction
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13205258

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.