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

Energies

Eui-Jong Kim¹  Byung-ki Jeon¹ 

[1] Department of Architectural Engineering, Inha University, Incheon 22212, Korea;

关键词: solar irradiance;    long short-term memory;    weather prediction;   

DOI  :  10.3390/en13205258

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown