CWEEE  Vol.9 No.4 , October 2020
Combination of WRF Model and LSTM Network for Solar Radiation Forecasting—Timor Leste Case Study
Abstract: A study of a combination of Weather Research and Forecasting (WRF) model and Long Short Term Memory (LSTM) network for location in Dili Timor Leste is introduced in this paper. One calendar year’s results of solar radiation from January to December 2014 are used as input data to estimate future forecasting of solar radiation using the LSTM network for three months period. The WRF model version 3.9.1 is used to simulate one year’s solar radiation in horizontal resolution low scale for nesting domain 1 × 1 km. It is done by applying 6-hourly interval 1º × 1º NCEP FNL analysis data used as Global Forecast System (GFS). LSTM network is applied for forecasting in numerous learning problems for solar radiation forecasting. LSTM network uses two-layer LSTM architecture of 512 hidden neurons coupled with a dense output layer with linear as the model activation to predict with time steps are configured to 50 and the number of features is 1. The maximum epoch is set to 325 with batch size 300 and the validation split is 0.09. The results demonstrate that the combination of these two methods can successfully predict solar radiation where four error metrics of mean bias error (MBE), root mean square error (RMSE), normalized MBE (nMBE), and normalized RMSE (nRMSE) perform small error distribution and percentage in three months prediction where the error percentage is obtained below the 20% for nMBE and nRMSE. Meanwhile, the error distribution of RMSE is obtained below 200 W/m2 and maximum bias error is 0.07. Finally, the values of MBE, RMSE, nMBE, and nRMSE conclude that the good performance of the combination of two methods in this study can be applied to simulate any other weather variable for local necessary.
Cite this paper: Araujo, J. (2020) Combination of WRF Model and LSTM Network for Solar Radiation Forecasting—Timor Leste Case Study. Computational Water, Energy, and Environmental Engineering, 9, 108-144. doi: 10.4236/cweee.2020.94009.

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