TITLE:
Combination of WRF Model and LSTM Network for Solar Radiation Forecasting—Timor Leste Case Study
AUTHORS:
Jose Manuel Soares de Araujo
KEYWORDS:
Combination, LSTM, Solar Radiation, WRF, Timor Leste
JOURNAL NAME:
Computational Water, Energy, and Environmental Engineering,
Vol.9 No.4,
September
11,
2020
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.